Journal Description
Micromachines
Micromachines
is a peer-reviewed, open access journal on the science and technology of small structures, devices and systems, published monthly online by MDPI. The Chinese Society of Micro-Nano Technology (CSMNT) is affiliated with Micromachines and its members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, Ei Compendex, dblp, and other databases.
- Journal Rank: JCR - Q2 (Physics, Applied) / CiteScore - Q2 (Mechanical Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.5 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Micromachines.
- Companion journal: Micro.
Impact Factor:
3.0 (2023);
5-Year Impact Factor:
3.0 (2023)
Latest Articles
Broadband S-Parameter-Based Characterization of Multilayer Ceramic Capacitors Submitted to Mechanical Stress Through Bending Tests on a PCB
Micromachines 2024, 15(11), 1386; https://doi.org/10.3390/mi15111386 (registering DOI) - 16 Nov 2024
Abstract
A full characterization of multilayer ceramic capacitors including variations in capacitance, series resistance, and series inductance is accomplished by measuring their RF response while being submitted to mechanical stress. This allows for the first time quantifying the degradation of the device’s RF performance
[...] Read more.
A full characterization of multilayer ceramic capacitors including variations in capacitance, series resistance, and series inductance is accomplished by measuring their RF response while being submitted to mechanical stress. This allows for the first time quantifying the degradation of the device’s RF performance when cracks form within its structure. In this regard, the main challenge is designing an interface for measuring the high-frequency response of a capacitor using a vector network analyzer as a bending test on a PCB in progress, which is achieved here by using a microstrip-based test fixture. The results indicate that there is an overestimation of its response to microwave stimuli when considering only the degradation impact as a reduction in capacitance. Capacitors of representative sizes and capacitances are analyzed to show the usefulness of the proposal, whereas the validity of the results is verified by observing the correlation with measurements collected using microprobes and performing optical inspections of cross-sectioned samples.
Full article
(This article belongs to the Section E:Engineering and Technology)
Open AccessArticle
Integrating Multiple Hierarchical Parameters to Achieve the Self-Compensation of Scale Factor in a Micro-Electromechanical System Gyroscope
by
Rui Zhou, Rang Cui, Daren An, Chong Shen, Yu Bai and Huiliang Cao
Micromachines 2024, 15(11), 1385; https://doi.org/10.3390/mi15111385 (registering DOI) - 16 Nov 2024
Abstract
The scale factor of thermal sensitivity serves as a crucial performance metric for micro-electromechanical system (MEMS) gyroscopes, and is commonly employed to assess the temperature stability of inertial sensors. To improve the temperature stability of the scale factor of MEMS gyroscopes, a self-compensation
[...] Read more.
The scale factor of thermal sensitivity serves as a crucial performance metric for micro-electromechanical system (MEMS) gyroscopes, and is commonly employed to assess the temperature stability of inertial sensors. To improve the temperature stability of the scale factor of MEMS gyroscopes, a self-compensation method is proposed. This is achieved by integrating the primary and secondary relevant parameters of the scale factor using the partial least squares regression (PLSR) algorithm. In this paper, a scale factor prediction model is presented. The model indicates that the resonant frequency and demodulation phase angle are the primary correlation terms of the scale factor, while the drive control voltage and quadrature feedback voltage are the secondary correlation terms of the scale factor. By employing a weighted fusion of correlated terms through PLSR, the scale factor for temperature sensitivity is markedly enhanced by leveraging the predicted results to compensate for the output. The results indicate that the maximum error of the predicted scale factor is 0.124% within the temperature range of −40 °C to 60 °C, and the temperature sensitivity of the scale factor decreases from 6180 ppm/°C to 9.39 ppm/°C.
Full article
(This article belongs to the Special Issue MEMS Sensors and Actuators: Design, Fabrication and Applications)
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Figure 1
<p>SVRG structure chip diagram.</p> Full article ">Figure 2
<p>(<b>a</b>) Vibrational form of drive mode. (<b>b</b>) Vibrational form of sense mode. SVRG’s primary and secondary modal vibration forms.</p> Full article ">Figure 3
<p>Electrode distribution of SVRG.</p> Full article ">Figure 4
<p>Mechanical model of SVRG.</p> Full article ">Figure 5
<p>Block diagram of gyroscope’s sense mode of operation.</p> Full article ">Figure 6
<p>Heatmap of correlation analysis for each parameter.</p> Full article ">Figure 7
<p>Scale factor prediction results.</p> Full article ">Figure 8
<p>(<b>a</b>) Gyro chip package size. (<b>b</b>) Gyro physical structure. (<b>c</b>) Hardware circuit of gyro self-compensation system. Gyro self-compensation system composition.</p> Full article ">Figure 9
<p>Block diagram of self-compensating system.</p> Full article ">Figure 10
<p>Test experiment environment setup.</p> Full article ">Figure 11
<p>Test results of each parameter across a wide temperature range.</p> Full article ">Figure 12
<p>Comparison of scale factor temperature sensitivity results before and after SVRG compensation.</p> Full article ">Figure 13
<p>Gyroscope zero-bias stability test results before and after compensation.</p> Full article ">
<p>SVRG structure chip diagram.</p> Full article ">Figure 2
<p>(<b>a</b>) Vibrational form of drive mode. (<b>b</b>) Vibrational form of sense mode. SVRG’s primary and secondary modal vibration forms.</p> Full article ">Figure 3
<p>Electrode distribution of SVRG.</p> Full article ">Figure 4
<p>Mechanical model of SVRG.</p> Full article ">Figure 5
<p>Block diagram of gyroscope’s sense mode of operation.</p> Full article ">Figure 6
<p>Heatmap of correlation analysis for each parameter.</p> Full article ">Figure 7
<p>Scale factor prediction results.</p> Full article ">Figure 8
<p>(<b>a</b>) Gyro chip package size. (<b>b</b>) Gyro physical structure. (<b>c</b>) Hardware circuit of gyro self-compensation system. Gyro self-compensation system composition.</p> Full article ">Figure 9
<p>Block diagram of self-compensating system.</p> Full article ">Figure 10
<p>Test experiment environment setup.</p> Full article ">Figure 11
<p>Test results of each parameter across a wide temperature range.</p> Full article ">Figure 12
<p>Comparison of scale factor temperature sensitivity results before and after SVRG compensation.</p> Full article ">Figure 13
<p>Gyroscope zero-bias stability test results before and after compensation.</p> Full article ">
Open AccessArticle
Quantum Channel Extreme Bandgap AlGaN HEMT
by
Michael Shur, Grigory Simin, Kamal Hussain, Abdullah Mamun, M. V. S. Chandrashekhar and Asif Khan
Micromachines 2024, 15(11), 1384; https://doi.org/10.3390/mi15111384 (registering DOI) - 15 Nov 2024
Abstract
An extreme bandgap Al0.64Ga0.36N quantum channel HEMT with Al0.87Ga0.13N top and back barriers, grown by MOCVD on a bulk AlN substrate, demonstrated a critical breakdown field of 11.37 MV/cm—higher than the 9.8 MV/cm expected for
[...] Read more.
An extreme bandgap Al0.64Ga0.36N quantum channel HEMT with Al0.87Ga0.13N top and back barriers, grown by MOCVD on a bulk AlN substrate, demonstrated a critical breakdown field of 11.37 MV/cm—higher than the 9.8 MV/cm expected for the channel’s Al0.64Ga0.36N material. We show that the fraction of this increase is due to the quantization of the 2D electron gas. The polarization field maintains electron quantization in the quantum channel even at low sheet densities, in contrast to conventional HEMT designs. An additional increase in the breakdown field is due to quantum-enabled real space transfer of energetic electrons into high-Al barrier layers in high electric fields. These results show the advantages of the quantum channel design for achieving record-high breakdown voltages and allowing for superior power HEMT devices.
Full article
(This article belongs to the Special Issue RF and Power Electronic Devices and Applications)
Open AccessArticle
Comparison of Interfaces Between In Situ Laser Beam Deposition Forming and Electron Beam Welding for Thick-Walled Titanium Alloy Structures
by
Pingchuan Yang, Fei Li, Zongtao Zhu and Hui Chen
Micromachines 2024, 15(11), 1383; https://doi.org/10.3390/mi15111383 (registering DOI) - 15 Nov 2024
Abstract
An investigation was conducted on electron beam-welded and additively manufactured joints on a thick-walled titanium alloy utilizing in situ laser beam deposition and electron beam welding techniques. The surface morphology, microstructural characteristics, and mechanical properties of both joint types were comprehensively analyzed using
[...] Read more.
An investigation was conducted on electron beam-welded and additively manufactured joints on a thick-walled titanium alloy utilizing in situ laser beam deposition and electron beam welding techniques. The surface morphology, microstructural characteristics, and mechanical properties of both joint types were comprehensively analyzed using stereomicroscopy, scanning electron microscopy (SEM), microhardness and tensile strength testing, and electron backscatter diffraction (EBSD) techniques. The electron-beam-welded joint exhibited distinct fusion and heat-affected zones, whereas the laser-beam-deposited joint exhibited a smoother surface that was free from excess spatter. Both joints featured a sharp microstructural boundary with a pronounced hardness gradient across the interface, lacking a gradual transition area. During tensile testing, both joint types demonstrated a mixed brittle-ductile fracture mode; however, the electron beam-welded joints surpassed the laser-beam-deposited joints in terms of tensile strength, achieving over 1183 MPa with an elongation of more than 7.3%, compared to 1123 MPa and 5.9% elongation, respectively.
Full article
(This article belongs to the Special Issue Future Prospects of Additive Manufacturing)
Open AccessReview
Exploring the Connection Between Nanomaterials and Neurodegenerative Disorders
by
Sitansu Sekhar Nanda and Dong Kee Yi
Micromachines 2024, 15(11), 1382; https://doi.org/10.3390/mi15111382 - 15 Nov 2024
Abstract
Drug delivery, tissue engineering, and cell promotion in biomedical fields heavily rely on the use of nanomaterials (NMs). When they penetrate cells, NPs undergo degradation and initiate the generation of reactive oxygen species (ROS) by causing changes in the structures of organelles linked
[...] Read more.
Drug delivery, tissue engineering, and cell promotion in biomedical fields heavily rely on the use of nanomaterials (NMs). When they penetrate cells, NPs undergo degradation and initiate the generation of reactive oxygen species (ROS) by causing changes in the structures of organelles linked to mitochondria. Inside the cell, the excess production of ROS can initiate a chain reaction, along with the autophagy process that helps maintain ROS balance by discarding unnecessary materials. At present, there is no effective treatment for Alzheimer’s disease (AD), a progressive neurodegenerative disease. The use of NMs for siRNA delivery could become a promising treatment for AD and other CNS disorders. Recent research demonstrates that the use of combined NPs can induce autophagy in cells. This article emphasizes the importance of the shape of siRNA-encapsulated NMs in determining their efficiency in delivering and suppressing gene activity in the central nervous system. Because of its strict selectivity against foreign substances, the blood–brain barrier (BBB) significantly hinders the delivery of therapeutic agents to the brain. Conventional chemotherapeutic drugs are significantly less effective against brain cancers due to this limitation. As a result, NMs have become a promising approach for targeted drug delivery, as they can be modified to carry specific ligands that direct them to their intended targets. This review thoroughly examines the latest breakthroughs in using NMs to deliver bioactive compounds across the BBB, focusing on their use in cancer treatments. The review starts by examining the structure and functions of the BBB and BBTB, and then emphasizes the benefits that NMs offer.
Full article
(This article belongs to the Section B3: Nanoparticles in Biomedicine)
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<p>A summary of how induced pluripotent stem cells are used to model the blood–brain barrier. This figure was adapted from reference [<a href="#B5-micromachines-15-01382" class="html-bibr">5</a>] with permission.</p> Full article ">Figure 2
<p>(<b>A</b>) Models employed to examine nanocarriers’ passage across the BBB. (<b>B</b>) Steps to construct in vivo BBB models. This figure was adapted from reference [<a href="#B9-micromachines-15-01382" class="html-bibr">9</a>] with permission.</p> Full article ">Figure 3
<p>The Human Protein Atlas provides information on the tissue-level protein expression of APP, BACE1, presenilin-1, and tau, indicating potential for Aβ production. This figure was adapted from reference [<a href="#B20-micromachines-15-01382" class="html-bibr">20</a>] with permission.</p> Full article ">Figure 4
<p>The biological composition of (<b>A</b>) the blood–brain barrier (BBB) and (<b>B</b>) the blood–brain tumor barrier (BBTB). This figure was adapted from reference [<a href="#B69-micromachines-15-01382" class="html-bibr">69</a>] with permission.</p> Full article ">
<p>A summary of how induced pluripotent stem cells are used to model the blood–brain barrier. This figure was adapted from reference [<a href="#B5-micromachines-15-01382" class="html-bibr">5</a>] with permission.</p> Full article ">Figure 2
<p>(<b>A</b>) Models employed to examine nanocarriers’ passage across the BBB. (<b>B</b>) Steps to construct in vivo BBB models. This figure was adapted from reference [<a href="#B9-micromachines-15-01382" class="html-bibr">9</a>] with permission.</p> Full article ">Figure 3
<p>The Human Protein Atlas provides information on the tissue-level protein expression of APP, BACE1, presenilin-1, and tau, indicating potential for Aβ production. This figure was adapted from reference [<a href="#B20-micromachines-15-01382" class="html-bibr">20</a>] with permission.</p> Full article ">Figure 4
<p>The biological composition of (<b>A</b>) the blood–brain barrier (BBB) and (<b>B</b>) the blood–brain tumor barrier (BBTB). This figure was adapted from reference [<a href="#B69-micromachines-15-01382" class="html-bibr">69</a>] with permission.</p> Full article ">
Open AccessReview
A Review of Ku-Band GaN HEMT Power Amplifiers Development
by
Jihoon Kim
Micromachines 2024, 15(11), 1381; https://doi.org/10.3390/mi15111381 - 15 Nov 2024
Abstract
This review article investigates the current status and advances in Ku-band gallium nitride (GaN) high-electron mobility transistor (HEMT) high-power amplifiers (HPAs), which are critical for satellite communications, unmanned aerial vehicle (UAV) systems, and military radar applications. The demand for high-frequency, high-power amplifiers is
[...] Read more.
This review article investigates the current status and advances in Ku-band gallium nitride (GaN) high-electron mobility transistor (HEMT) high-power amplifiers (HPAs), which are critical for satellite communications, unmanned aerial vehicle (UAV) systems, and military radar applications. The demand for high-frequency, high-power amplifiers is growing, driven by the global expansion of high-speed data communication and enhanced national security requirements. First, we compare the main GaN HEMT process technologies employed in Ku-band HPA development, categorizing the HPAs into monolithic microwave integrated circuits (MMICs) and internally matched power amplifier modules (IM-PAMs) and examining their respective characteristics. Then, by reviewing the literature, we explore design topologies, major issues like oscillation prevention and bias circuits, and heat sink technologies for thermal management. Our findings indicate that silicon carbide (SiC) substrates with gate lengths of 0.25 μm and 0.15 μm are predominantly used, with ongoing developments enabling MMICs and IM-PAMs to achieve up to 100 W output power and 30% power-added efficiency. Notably, the performance of MMIC power amplifiers is advancing more rapidly than that of IM-PAMs, highlighting MMICs as a promising direction for achieving higher efficiency and integration in future Ku-band applications. This paper can provide insights into the overall key technologies for Ku-band GaN HPA design and future development directions.
Full article
(This article belongs to the Special Issue Novel Power Amplifiers and Integrated Circuits: Design and Applications)
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Figure 1
<p>Various applications of satellite communications. Figure reproduced from [<a href="#B4-micromachines-15-01381" class="html-bibr">4</a>].</p> Full article ">Figure 2
<p>Comparison of breakdown voltage and cutoff frequency among various high-speed semiconductor devices [<a href="#B9-micromachines-15-01381" class="html-bibr">9</a>,<a href="#B10-micromachines-15-01381" class="html-bibr">10</a>]. (Data from [<a href="#B9-micromachines-15-01381" class="html-bibr">9</a>,<a href="#B10-micromachines-15-01381" class="html-bibr">10</a>]).</p> Full article ">Figure 3
<p>Cross-sectional structure of a typical GaN HEMT [<a href="#B13-micromachines-15-01381" class="html-bibr">13</a>].</p> Full article ">Figure 4
<p>(<b>a</b>) Structure and (<b>b</b>) circuit schematic of conventional GaN HEMT HPA MMIC. Figures reproduced or reworked with permission from ref. [<a href="#B33-micromachines-15-01381" class="html-bibr">33</a>]. Copyright 2023 MDPI.</p> Full article ">Figure 5
<p>(<b>a</b>) OSV and (<b>b</b>) ISV layouts of a 4 × 50 μm GaN HEMT.</p> Full article ">Figure 6
<p>Design example of a Ku-band MIC HPA implemented with an internal matching approach. Figures reproduced with permission from ref. [<a href="#B43-micromachines-15-01381" class="html-bibr">43</a>]. Copyright 2018 MDPI.</p> Full article ">Figure 7
<p>Example photos of (<b>a</b>) a fabricated Ku-band GaN HEMT IM-PAM and (<b>b</b>) a GaN HEMT power amplifier module with wire bonding. Figures reproduced or reworked with permission from refs. [<a href="#B38-micromachines-15-01381" class="html-bibr">38</a>,<a href="#B43-micromachines-15-01381" class="html-bibr">43</a>]. (<b>a</b>) is Copyright 2018 MDPI and (<b>b</b>) is Copyright 2023 IEEE.</p> Full article ">Figure 8
<p>Temperature distribution of a 20 W class GaN HEMT HPA bare die (<b>a</b>) with only DC power applied and (<b>b</b>) with DC power and RF power applied using a high-resolution IR scope.</p> Full article ">Figure 9
<p>Heat sink structure of a GaN HEMT die [<a href="#B44-micromachines-15-01381" class="html-bibr">44</a>].</p> Full article ">Figure 10
<p>Comparison of output power and PAE of GaN HEMT MMICs according to thermal interface material and heat spreader combinations.</p> Full article ">
<p>Various applications of satellite communications. Figure reproduced from [<a href="#B4-micromachines-15-01381" class="html-bibr">4</a>].</p> Full article ">Figure 2
<p>Comparison of breakdown voltage and cutoff frequency among various high-speed semiconductor devices [<a href="#B9-micromachines-15-01381" class="html-bibr">9</a>,<a href="#B10-micromachines-15-01381" class="html-bibr">10</a>]. (Data from [<a href="#B9-micromachines-15-01381" class="html-bibr">9</a>,<a href="#B10-micromachines-15-01381" class="html-bibr">10</a>]).</p> Full article ">Figure 3
<p>Cross-sectional structure of a typical GaN HEMT [<a href="#B13-micromachines-15-01381" class="html-bibr">13</a>].</p> Full article ">Figure 4
<p>(<b>a</b>) Structure and (<b>b</b>) circuit schematic of conventional GaN HEMT HPA MMIC. Figures reproduced or reworked with permission from ref. [<a href="#B33-micromachines-15-01381" class="html-bibr">33</a>]. Copyright 2023 MDPI.</p> Full article ">Figure 5
<p>(<b>a</b>) OSV and (<b>b</b>) ISV layouts of a 4 × 50 μm GaN HEMT.</p> Full article ">Figure 6
<p>Design example of a Ku-band MIC HPA implemented with an internal matching approach. Figures reproduced with permission from ref. [<a href="#B43-micromachines-15-01381" class="html-bibr">43</a>]. Copyright 2018 MDPI.</p> Full article ">Figure 7
<p>Example photos of (<b>a</b>) a fabricated Ku-band GaN HEMT IM-PAM and (<b>b</b>) a GaN HEMT power amplifier module with wire bonding. Figures reproduced or reworked with permission from refs. [<a href="#B38-micromachines-15-01381" class="html-bibr">38</a>,<a href="#B43-micromachines-15-01381" class="html-bibr">43</a>]. (<b>a</b>) is Copyright 2018 MDPI and (<b>b</b>) is Copyright 2023 IEEE.</p> Full article ">Figure 8
<p>Temperature distribution of a 20 W class GaN HEMT HPA bare die (<b>a</b>) with only DC power applied and (<b>b</b>) with DC power and RF power applied using a high-resolution IR scope.</p> Full article ">Figure 9
<p>Heat sink structure of a GaN HEMT die [<a href="#B44-micromachines-15-01381" class="html-bibr">44</a>].</p> Full article ">Figure 10
<p>Comparison of output power and PAE of GaN HEMT MMICs according to thermal interface material and heat spreader combinations.</p> Full article ">
Open AccessArticle
Research on Energy Dissipation Mechanism of Cobweb-like Disk Resonator Gyroscope
by
Huang Yi, Bo Fan, Feng Bu, Fang Chen and Xiao-Qing Luo
Micromachines 2024, 15(11), 1380; https://doi.org/10.3390/mi15111380 - 15 Nov 2024
Abstract
The micro disk resonator gyroscope is a micro-mechanical device with potential for navigation-grade applications, where the performance is significantly influenced by the quality factor, which is determined by various energy dissipation mechanisms within the micro resonant structure. To enhance the quality factor, these
[...] Read more.
The micro disk resonator gyroscope is a micro-mechanical device with potential for navigation-grade applications, where the performance is significantly influenced by the quality factor, which is determined by various energy dissipation mechanisms within the micro resonant structure. To enhance the quality factor, these gyroscopes are typically enclosed in high-vacuum packaging. This paper investigates a wafer-level high-vacuum-packaged (<0.1 Pa) cobweb-like disk resonator gyroscope, presenting a systematic and comprehensive theoretical analysis of the energy dissipation mechanisms, including air damping, thermoelastic damping, anchor loss, and other factors. Air damping is analyzed using both a continuous fluid model and an energy transfer model. The analysis results are validated through quality factor testing on batch samples and temperature characteristic testing on individual samples. The theoretical results obtained using the energy transfer model closely match the experimental measurements, with a maximum error in the temperature coefficient of less than 2%. The findings indicate that air damping and thermoelastic damping are the predominant energy dissipation mechanisms in the cobweb-like disk resonant gyroscope under high-vacuum conditions. Consequently, optimizing the resonator to minimize thermoelastic and air damping is crucial for designing high-performance gyroscopes.
Full article
(This article belongs to the Special Issue Advances in MEMS Inertial Sensors)
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<p>A cobweb-like disk resonator gyroscope (CDRG) structure schematic diagram with mass and stiffness decoupling.</p> Full article ">Figure 2
<p>Mode shapes of the <span class="html-italic">n</span> = 2 wineglass modes of CDRG.</p> Full article ">Figure 3
<p>Schematic diagram of the squeeze film damping effect on a long rectangular plate.</p> Full article ">Figure 4
<p>Pressure distribution diagram of squeeze film damping between long rectangular plates.</p> Full article ">Figure 5
<p>Gas particles between a substrate and a moving plate for the energy transfer model.</p> Full article ">Figure 6
<p>The relationship between the damping of the two models and the pressure, and <span class="html-italic">k</span> is the error coefficient.</p> Full article ">Figure 7
<p>The relationship between <span class="html-italic">Q<sub>TED</sub></span>, ring width, and vibration frequency.</p> Full article ">Figure 8
<p>The temperature gradient distribution in the working mode of CDRG, with the purple arrows indicating the heat flow path.</p> Full article ">Figure 9
<p>The grid dissection results of PMLs for a CDRG.</p> Full article ">Figure 10
<p>A simplified schematic diagram of the circuit model for the experimental setup of the CDRG.</p> Full article ">Figure 11
<p>Quality factor test results of CDRG # 1 at room temperature (<span class="html-italic">T</span> = 298.15 K). (<b>a</b>) Drive mode; (<b>b</b>) sense mode.</p> Full article ">Figure 12
<p>The relation between the total energy dissipation mechanism of the modified continuous fluid model (CFM) and the energy transfer model (EFM) at room temperature (<span class="html-italic">T</span> = 298.15 K).</p> Full article ">Figure 13
<p>Comparison of measured results and theoretical models of device CDRG # 3.</p> Full article ">
<p>A cobweb-like disk resonator gyroscope (CDRG) structure schematic diagram with mass and stiffness decoupling.</p> Full article ">Figure 2
<p>Mode shapes of the <span class="html-italic">n</span> = 2 wineglass modes of CDRG.</p> Full article ">Figure 3
<p>Schematic diagram of the squeeze film damping effect on a long rectangular plate.</p> Full article ">Figure 4
<p>Pressure distribution diagram of squeeze film damping between long rectangular plates.</p> Full article ">Figure 5
<p>Gas particles between a substrate and a moving plate for the energy transfer model.</p> Full article ">Figure 6
<p>The relationship between the damping of the two models and the pressure, and <span class="html-italic">k</span> is the error coefficient.</p> Full article ">Figure 7
<p>The relationship between <span class="html-italic">Q<sub>TED</sub></span>, ring width, and vibration frequency.</p> Full article ">Figure 8
<p>The temperature gradient distribution in the working mode of CDRG, with the purple arrows indicating the heat flow path.</p> Full article ">Figure 9
<p>The grid dissection results of PMLs for a CDRG.</p> Full article ">Figure 10
<p>A simplified schematic diagram of the circuit model for the experimental setup of the CDRG.</p> Full article ">Figure 11
<p>Quality factor test results of CDRG # 1 at room temperature (<span class="html-italic">T</span> = 298.15 K). (<b>a</b>) Drive mode; (<b>b</b>) sense mode.</p> Full article ">Figure 12
<p>The relation between the total energy dissipation mechanism of the modified continuous fluid model (CFM) and the energy transfer model (EFM) at room temperature (<span class="html-italic">T</span> = 298.15 K).</p> Full article ">Figure 13
<p>Comparison of measured results and theoretical models of device CDRG # 3.</p> Full article ">
Open AccessArticle
Multi-Frame Vibration MEMS Gyroscope Temperature Compensation Based on Combined GWO-VMD-TCN-LSTM Algorithm
by
Ao Li, Ke Cui, Daren An, Xiaoyi Wang and Huiliang Cao
Micromachines 2024, 15(11), 1379; https://doi.org/10.3390/mi15111379 - 15 Nov 2024
Abstract
This paper presents a temperature compensation model for the Multi-Frame Vibration MEMS Gyroscope (DMFVMG) based on Grey Wolf Optimization Variational Mode Decomposition (GWO-VMD) for denoising and a combination of the Temporal Convolutional Network (TCN) and the Long Short-Term Memory (LSTM) network for temperature
[...] Read more.
This paper presents a temperature compensation model for the Multi-Frame Vibration MEMS Gyroscope (DMFVMG) based on Grey Wolf Optimization Variational Mode Decomposition (GWO-VMD) for denoising and a combination of the Temporal Convolutional Network (TCN) and the Long Short-Term Memory (LSTM) network for temperature drift prediction. Initially, the gyroscope output signal was denoised using GWO-VMD, retaining the useful signal components and eliminating noise. Subsequently, the denoised signal was utilized to predict temperature drift using the TCN-LSTM model. The experimental results demonstrate that the compensation model significantly enhanced the gyroscope’s performance across various temperatures, reducing the rate random wander from 102.929°/h/√Hz to 17.6903°/h/√Hz and the bias instability from 63.70°/h to 1.38°/h, with reductions of 82.81% and 97.83%, respectively. This study validates the effectiveness and superiority of the proposed temperature compensation model.
Full article
(This article belongs to the Special Issue MEMS Sensors and Actuators: Design, Fabrication and Applications)
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<p>The diagrammatic representation of the DMFVMG structure.</p> Full article ">Figure 2
<p>Analysis of the modal behavior of the DMFVMG structure.</p> Full article ">Figure 3
<p>The control block diagram of DMFVMG gyro.</p> Full article ">Figure 4
<p>Combined gyroscope temperature drift signal prediction models.</p> Full article ">Figure 5
<p>Schematic diagram of the gyroscope temperature compensation model.</p> Full article ">Figure 6
<p>(<b>a</b>) Field emission scanning electron microscope (FESEM) image (color added). (<b>b</b>) DMFVMG prototype photo. (<b>c</b>) Experimental environment settings.</p> Full article ">Figure 7
<p>(<b>a</b>) Data from the training phase of the experiment. (<b>b</b>) Data from the testing phase of the experiment.</p> Full article ">Figure 8
<p>Decomposition results and spectrograms based on GWO-VMD.</p> Full article ">Figure 9
<p>Comparison of DMFVMG signal before and after denoising.</p> Full article ">Figure 10
<p>Comparison of effect after temperature compensation.</p> Full article ">Figure 11
<p>Graph of Allan ANOVA result.</p> Full article ">
<p>The diagrammatic representation of the DMFVMG structure.</p> Full article ">Figure 2
<p>Analysis of the modal behavior of the DMFVMG structure.</p> Full article ">Figure 3
<p>The control block diagram of DMFVMG gyro.</p> Full article ">Figure 4
<p>Combined gyroscope temperature drift signal prediction models.</p> Full article ">Figure 5
<p>Schematic diagram of the gyroscope temperature compensation model.</p> Full article ">Figure 6
<p>(<b>a</b>) Field emission scanning electron microscope (FESEM) image (color added). (<b>b</b>) DMFVMG prototype photo. (<b>c</b>) Experimental environment settings.</p> Full article ">Figure 7
<p>(<b>a</b>) Data from the training phase of the experiment. (<b>b</b>) Data from the testing phase of the experiment.</p> Full article ">Figure 8
<p>Decomposition results and spectrograms based on GWO-VMD.</p> Full article ">Figure 9
<p>Comparison of DMFVMG signal before and after denoising.</p> Full article ">Figure 10
<p>Comparison of effect after temperature compensation.</p> Full article ">Figure 11
<p>Graph of Allan ANOVA result.</p> Full article ">
Open AccessArticle
Analysis of the Radial Force of a Piezoelectric Actuator with Interdigitated Spiral Electrodes
by
Yateng Wang, Tianxing Ren, Yuan Ren, Ruijie Gu and Yonggang Liu
Micromachines 2024, 15(11), 1378; https://doi.org/10.3390/mi15111378 - 15 Nov 2024
Abstract
The actuator is a critical component of the micromanipulator. By utilizing the properties of expansion and contraction, the piezoelectric actuator enables the manipulator to handle and grasp miniature objects during micromanipulation. However, in piezoelectric ceramic disc actuators with conventional surface electrode configurations, the
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The actuator is a critical component of the micromanipulator. By utilizing the properties of expansion and contraction, the piezoelectric actuator enables the manipulator to handle and grasp miniature objects during micromanipulation. However, in piezoelectric ceramic disc actuators with conventional surface electrode configurations, the actuating force generated in the radial direction is relatively limited. When used as the actuation element of the manipulator, achieving regulation over a wide range of operating strokes becomes challenging. Therefore, altering the electrode structure is necessary to generate a greater radial force, thus enhancing the positioning and grasping capabilities of the operating arm. This paper investigates a piezoelectric actuator with interdigitated spiral electrodes, featuring a constant pitch between adjacent electrodes. The radial force was tested under mechanical clamping conditions, and the influence of the electrical signal was examined. The characteristics of the electrode structure were described, and the working principles of the piezoelectric actuators were analyzed. Theoretical equations were derived for the macroscopic characterization of the radial clamping force of the actuator, based on the piezoelectric constitutive equation, geometric principles, and Bond matrix transformation relationships. A finite element model was developed, reflecting the features of the electrode structure, and finite element simulations were employed to verify the theoretical equations for radial force. To prepare the samples, encircled interdigitated spiral electrode lines were printed on the PZT-52 piezoelectric ceramic disc using a screen printing method. The clamping force experimental platform was established, and experiments on the clamping radial force were conducted with electrical signals of varying waveforms, frequencies, and voltages. The experimental results show that the piezoelectric ceramic disc actuator with an interdigitated spiral electrode line structure, when excited by a stable sine wave operating at 200 V and 0.2 Hz, generated a peak force of 0.37 N. It was 1.76 times greater than that produced by a previously utilized piezoelectric disc with conventional electrode structures.
Full article
(This article belongs to the Special Issue Soft Actuators: Design, Fabrication and Applications, 2nd Edition)
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<p>Structure of the piezoelectric actuator with spiral electrodes.</p> Full article ">Figure 2
<p>The deformation diagram of the electrode spacing sub-body.</p> Full article ">Figure 3
<p>Overall model and segmentation models.</p> Full article ">Figure 4
<p>Diagram of the finite element meshing model.</p> Full article ">Figure 5
<p>Radial stress distribution.</p> Full article ">Figure 6
<p>Flowchart for polarization and preparation of samples.</p> Full article ">Figure 7
<p>Schematic diagram of the force test.</p> Full article ">Figure 8
<p>Clamping force experimental device: 1. Antai ATA-2021 High Voltage Amplifier (Aigtek Electronic Technology Ltd., Xian, China); 2. Tektronix AFG1022 Signal Generator (Tektronix, Inc., Johnston, IA, USA); 3. Samples; 4. Digital force gauge (YueQing Handpi Instruments Co., Ltd., Yueqing, China); 5. Computer; 6. Damping block.</p> Full article ">Figure 9
<p>Influence of the electrical signal waveform on the radial force: (<b>a</b>) Force–time history versus the signals; (<b>b</b>) Force response versus the signals.</p> Full article ">Figure 10
<p>Influence of the voltage on the radial force: (<b>a</b>) Force–time history versus the voltage; (<b>b</b>) Force response versus the voltage.</p> Full article ">Figure 11
<p>Influence of the frequency on the radial force: (<b>a</b>) Force–time history versus the frequency of the electrical signal; (<b>b</b>) Force response versus the frequency of the electrical signal.</p> Full article ">
<p>Structure of the piezoelectric actuator with spiral electrodes.</p> Full article ">Figure 2
<p>The deformation diagram of the electrode spacing sub-body.</p> Full article ">Figure 3
<p>Overall model and segmentation models.</p> Full article ">Figure 4
<p>Diagram of the finite element meshing model.</p> Full article ">Figure 5
<p>Radial stress distribution.</p> Full article ">Figure 6
<p>Flowchart for polarization and preparation of samples.</p> Full article ">Figure 7
<p>Schematic diagram of the force test.</p> Full article ">Figure 8
<p>Clamping force experimental device: 1. Antai ATA-2021 High Voltage Amplifier (Aigtek Electronic Technology Ltd., Xian, China); 2. Tektronix AFG1022 Signal Generator (Tektronix, Inc., Johnston, IA, USA); 3. Samples; 4. Digital force gauge (YueQing Handpi Instruments Co., Ltd., Yueqing, China); 5. Computer; 6. Damping block.</p> Full article ">Figure 9
<p>Influence of the electrical signal waveform on the radial force: (<b>a</b>) Force–time history versus the signals; (<b>b</b>) Force response versus the signals.</p> Full article ">Figure 10
<p>Influence of the voltage on the radial force: (<b>a</b>) Force–time history versus the voltage; (<b>b</b>) Force response versus the voltage.</p> Full article ">Figure 11
<p>Influence of the frequency on the radial force: (<b>a</b>) Force–time history versus the frequency of the electrical signal; (<b>b</b>) Force response versus the frequency of the electrical signal.</p> Full article ">
Open AccessArticle
ScAlN PMUTs Based on Flexurally Suspended Membrane for Long-Range Detection
by
Shutao Yao, Wenling Shang, Guifeng Ta, Jinyan Tao, Haojie Liu, Xiangyong Zhao, Jianhe Liu, Bin Miao and Jiadong Li
Micromachines 2024, 15(11), 1377; https://doi.org/10.3390/mi15111377 - 14 Nov 2024
Abstract
Piezoelectric micromachined ultrasonic transducers (PMUTs) have been widely applied in distance sensing applications. However, the rapid movement of miniature robots in complex environments necessitates higher ranging capabilities from sensors, making the enhancement of PMUT sensing distance critically important. In this paper, a scandium-doped
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Piezoelectric micromachined ultrasonic transducers (PMUTs) have been widely applied in distance sensing applications. However, the rapid movement of miniature robots in complex environments necessitates higher ranging capabilities from sensors, making the enhancement of PMUT sensing distance critically important. In this paper, a scandium-doped aluminum nitride (ScAlN) PMUT based on a flexurally suspended membrane is proposed. Unlike the traditional fully clamped design, the PMUT incorporates a partially clamped membrane, thereby extending the vibration displacement and enhancing the output sound pressure. Experimental results demonstrate that at a resonant frequency of 78 kHz, a single PMUT generates a sound pressure level (SPL) of 112.2 dB at a distance of 10 mm and achieves a high receiving sensitivity of 12.3 mV/Pa. Distance testing reveals that a single PMUT equipped with a horn can achieve a record-breaking distance sensing range of 11.2 m when used alongside a device capable of simultaneously transmitting and receiving ultrasound signals. This achievement is significant for miniaturized and integrated applications that utilize ultrasound for long-range target detection.
Full article
(This article belongs to the Special Issue MEMS Ultrasonic Transducers)
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<p>(<b>a</b>) Structure of the PMUT; (<b>b</b>–<b>d</b>) cross-sectional structure of FS-PMUT with sealed grooves, T-PMUT, and FS-PMUT with open grooves.</p> Full article ">Figure 2
<p>(<b>a</b>) The first-order modal shapes of FS-PMUT with different numbers of grooves, including those with open grooves (top) and those with sealed grooves (bottom); (<b>b</b>) transmission sensitivity and reception sensitivity of FS-PMUT with different numbers of grooves, including those with open grooves (dashed line) and with sealed grooves (solid line); (<b>c</b>,<b>d</b>) transmission and reception sensitivity of FS-PMUT with four sealed grooves of different widths; (<b>e</b>,<b>f</b>) transmission and reception sensitivity of FS-PMUT with four sealed grooves of different angles.</p> Full article ">Figure 3
<p>(<b>a</b>) Equivalent circuit model, including the electrical domain (left) and mechanical domain (right); (<b>b</b>) schematic diagram of the boundary structure.</p> Full article ">Figure 4
<p>Simulated frequency responses of FS-PMUT and T-PMUT at their resonance frequencies: (<b>a</b>) mode shapes of FS-PMUT and T-PMUT; (<b>b</b>) center displacement of PMUT at 10 Vpp; (<b>c</b>) comparison of vibration displacement along the diameter direction of the PMUT. (<b>d</b>) Transmission sensitivity at a distance of 10 mm; (<b>e</b>) comparison of reception sensitivity.</p> Full article ">Figure 5
<p>(<b>a</b>–<b>f</b>) FS-PMUT (sealed groove) fabrication process flow; (<b>g</b>,<b>h</b>) scanning electron microscopy of the upper surface of FS-PMUT (sealed groove).</p> Full article ">Figure 6
<p>(<b>a</b>) Device dimensions (2 mm × 2 mm × 0.3 mm) relative to a coin and a conventional ultrasound transducer; (<b>b</b>–<b>d</b>) the optical images of the three PMUT structures mentioned in this work: (<b>b</b>) is FS-PMUT with sealed grooves, (<b>c</b>) is FS-PMUT with open grooves, (<b>d</b>) is T-PMUT; (<b>e</b>,<b>f</b>) the cross-sectional view taken through a scanning electron microscope.</p> Full article ">Figure 7
<p>LDV test results of FS-PMUT (R590) and T-PMUT (R715). (<b>a</b>) center displacement of PMUT at 10 Vpp; (<b>b</b>) the displacement along the diameter direction of the PMUT.</p> Full article ">Figure 8
<p>Measured electrical impedance values of a single PMUT in air. (<b>a</b>) electrical characteristics of FS-PMUT with sealed grooves; (<b>b</b>) electrical characteristics of FS-PMUT with open grooves; (<b>c</b>) electrical characteristics of T-PMUT.</p> Full article ">Figure 9
<p>(<b>a</b>) Schematic diagram of experimental set up; (<b>b</b>–<b>d</b>) comparison results of the reception sensitivity.</p> Full article ">Figure 10
<p>(<b>a</b>) The block diagram of the system; (<b>b</b>) experimental assembly for distance testing; (<b>c</b>) envelope curves of the echo signal during distance measurement using a single PMUT.</p> Full article ">Figure 11
<p>SNR versus distance: at a threshold of 12 dB, the maximum range of the FS-PMUT with sealed grooves is 11.2 m.</p> Full article ">
<p>(<b>a</b>) Structure of the PMUT; (<b>b</b>–<b>d</b>) cross-sectional structure of FS-PMUT with sealed grooves, T-PMUT, and FS-PMUT with open grooves.</p> Full article ">Figure 2
<p>(<b>a</b>) The first-order modal shapes of FS-PMUT with different numbers of grooves, including those with open grooves (top) and those with sealed grooves (bottom); (<b>b</b>) transmission sensitivity and reception sensitivity of FS-PMUT with different numbers of grooves, including those with open grooves (dashed line) and with sealed grooves (solid line); (<b>c</b>,<b>d</b>) transmission and reception sensitivity of FS-PMUT with four sealed grooves of different widths; (<b>e</b>,<b>f</b>) transmission and reception sensitivity of FS-PMUT with four sealed grooves of different angles.</p> Full article ">Figure 3
<p>(<b>a</b>) Equivalent circuit model, including the electrical domain (left) and mechanical domain (right); (<b>b</b>) schematic diagram of the boundary structure.</p> Full article ">Figure 4
<p>Simulated frequency responses of FS-PMUT and T-PMUT at their resonance frequencies: (<b>a</b>) mode shapes of FS-PMUT and T-PMUT; (<b>b</b>) center displacement of PMUT at 10 Vpp; (<b>c</b>) comparison of vibration displacement along the diameter direction of the PMUT. (<b>d</b>) Transmission sensitivity at a distance of 10 mm; (<b>e</b>) comparison of reception sensitivity.</p> Full article ">Figure 5
<p>(<b>a</b>–<b>f</b>) FS-PMUT (sealed groove) fabrication process flow; (<b>g</b>,<b>h</b>) scanning electron microscopy of the upper surface of FS-PMUT (sealed groove).</p> Full article ">Figure 6
<p>(<b>a</b>) Device dimensions (2 mm × 2 mm × 0.3 mm) relative to a coin and a conventional ultrasound transducer; (<b>b</b>–<b>d</b>) the optical images of the three PMUT structures mentioned in this work: (<b>b</b>) is FS-PMUT with sealed grooves, (<b>c</b>) is FS-PMUT with open grooves, (<b>d</b>) is T-PMUT; (<b>e</b>,<b>f</b>) the cross-sectional view taken through a scanning electron microscope.</p> Full article ">Figure 7
<p>LDV test results of FS-PMUT (R590) and T-PMUT (R715). (<b>a</b>) center displacement of PMUT at 10 Vpp; (<b>b</b>) the displacement along the diameter direction of the PMUT.</p> Full article ">Figure 8
<p>Measured electrical impedance values of a single PMUT in air. (<b>a</b>) electrical characteristics of FS-PMUT with sealed grooves; (<b>b</b>) electrical characteristics of FS-PMUT with open grooves; (<b>c</b>) electrical characteristics of T-PMUT.</p> Full article ">Figure 9
<p>(<b>a</b>) Schematic diagram of experimental set up; (<b>b</b>–<b>d</b>) comparison results of the reception sensitivity.</p> Full article ">Figure 10
<p>(<b>a</b>) The block diagram of the system; (<b>b</b>) experimental assembly for distance testing; (<b>c</b>) envelope curves of the echo signal during distance measurement using a single PMUT.</p> Full article ">Figure 11
<p>SNR versus distance: at a threshold of 12 dB, the maximum range of the FS-PMUT with sealed grooves is 11.2 m.</p> Full article ">
Open AccessArticle
An Image Processing Approach to Quality Control of Drop-on-Demand Electrohydrodynamic (EHD) Printing
by
Yahya Tawhari, Charchit Shukla and Juan Ren
Micromachines 2024, 15(11), 1376; https://doi.org/10.3390/mi15111376 - 14 Nov 2024
Abstract
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Droplet quality in drop-on-demand (DoD) Electrohydrodynamic (EHD) inkjet printing plays a crucial role in influencing the overall performance and manufacturing quality of the operation. The current approach to droplet printing analysis involves manually outlining/labeling the printed dots on the substrate under a microscope
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Droplet quality in drop-on-demand (DoD) Electrohydrodynamic (EHD) inkjet printing plays a crucial role in influencing the overall performance and manufacturing quality of the operation. The current approach to droplet printing analysis involves manually outlining/labeling the printed dots on the substrate under a microscope and then using microscope software to estimate the dot sizes by assuming the dots have a standard circular shape. Therefore, it is prone to errors. Moreover, the dot spacing information is missing, which is also important for EHD DoD printing processes, such as manufacturing micro-arrays. In order to address these issues, the paper explores the application of feature extraction methods aimed at identifying characteristics of the printed droplets to enhance the detection, evaluation, and delineation of significant structures and edges in printed images. The proposed method involves three main stages: (1) image pre-processing, where edge detection techniques such as Canny filtering are applied for printed dot boundary detection; (2) contour detection, which is used to accurately quantify the dot sizes (such as dot perimeter and area); and (3) centroid detection and distance calculation, where the spacing between neighboring dots is quantified as the Euclidean distance of the dot geometric centers. These stages collectively improve the precision and efficiency of EHD DoD printing analysis in terms of dot size and spacing. Edge and contour detection strategies are implemented to minimize edge discrepancies and accurately delineate droplet perimeters for quality analysis, enhancing measurement precision. The proposed image processing approach was first tested using simulated EHD printed droplet arrays with specified dot sizes and spacing, and the achieved quantification accuracy was over 98% in analyzing dot size and spacing, highlighting the high precision of the proposed approach. This approach was further demonstrated through dot analysis of experimentally EHD-printed droplets, showing its superiority over conventional microscope-based measurements.
Full article
Figure 1
Figure 1
<p>Overview of the proposed framework for DoD EHD printing analysis. (<b>a</b>) The proposed method takes a microscope image of printed droplets as the input, outputs the detection results, and calculates the spacing of the dot size (area). (<b>b</b>) Visual analysis of the printing quality provides size distribution of the printed dots. The x-axis represents the three categories of dot size range, and the y-axis indicates the percentage within each size category.</p> Full article ">Figure 2
<p>An image (<b>a</b>) is used as input for the pre-proposed method; (<b>b</b>) is the image after applying a Gaussian filter; (<b>c</b>) is the image after applying Canny edge detection; (<b>d</b>) is the image after applying dilation and morphological closing.</p> Full article ">Figure 3
<p>Line dot printing pattern. (<b>a</b>) original pattern. (<b>b</b>) processed pattern.</p> Full article ">Figure 4
<p>Distance Quantification Analysis.</p> Full article ">Figure 5
<p>Circular dot printing pattern. (<b>a</b>) original pattern. (<b>b</b>) processed pattern.</p> Full article ">Figure 6
<p>Experimental EHD DoD print result analysis. (<b>a</b>) The origin image of the printed droplet. (<b>b</b>) The printed droplet was analyzed offline using a manual microscope approach. (<b>c</b>) The analyzed image using the the proposed approach. (<b>d</b>) Detailed comparison of the two analysis methods.</p> Full article ">
<p>Overview of the proposed framework for DoD EHD printing analysis. (<b>a</b>) The proposed method takes a microscope image of printed droplets as the input, outputs the detection results, and calculates the spacing of the dot size (area). (<b>b</b>) Visual analysis of the printing quality provides size distribution of the printed dots. The x-axis represents the three categories of dot size range, and the y-axis indicates the percentage within each size category.</p> Full article ">Figure 2
<p>An image (<b>a</b>) is used as input for the pre-proposed method; (<b>b</b>) is the image after applying a Gaussian filter; (<b>c</b>) is the image after applying Canny edge detection; (<b>d</b>) is the image after applying dilation and morphological closing.</p> Full article ">Figure 3
<p>Line dot printing pattern. (<b>a</b>) original pattern. (<b>b</b>) processed pattern.</p> Full article ">Figure 4
<p>Distance Quantification Analysis.</p> Full article ">Figure 5
<p>Circular dot printing pattern. (<b>a</b>) original pattern. (<b>b</b>) processed pattern.</p> Full article ">Figure 6
<p>Experimental EHD DoD print result analysis. (<b>a</b>) The origin image of the printed droplet. (<b>b</b>) The printed droplet was analyzed offline using a manual microscope approach. (<b>c</b>) The analyzed image using the the proposed approach. (<b>d</b>) Detailed comparison of the two analysis methods.</p> Full article ">
Open AccessArticle
Effect of Hot Junction Size on the Temperature Measurement of Proton Exchange Membrane Fuel Cells Using NiCr/NiSi Thin-Film Thermocouple Sensors
by
Huijin Guo, Zhihui Liu, Tengda Guo, Yi Sun, Kai Shen, Bi Wang, Yongjun Cheng, Yuming Wang, Tiancai Ma, Zixi Wang and Wanyu Ding
Micromachines 2024, 15(11), 1375; https://doi.org/10.3390/mi15111375 - 14 Nov 2024
Abstract
In the process of using thin-film thermocouples for contact measurement of the reaction temperature in proton exchange membrane fuel cells (PEMFC), the impact of thin-film thermocouple volume on the system’s reaction temperature field variation, reaction efficiency, and the lifespan of thermocouples under these
[...] Read more.
In the process of using thin-film thermocouples for contact measurement of the reaction temperature in proton exchange membrane fuel cells (PEMFC), the impact of thin-film thermocouple volume on the system’s reaction temperature field variation, reaction efficiency, and the lifespan of thermocouples under these conditions is not thoroughly studied. Using magnetron sputtering technology, NiCr/NiSi thin-film thermocouples (NiCr/NiSi TFTCs) with different junction sizes were fabricated on the proton exchange membrane (PEM). These NiCr/NiSi TFTCs exhibit excellent compactness, with thickness and planar dimensions in the micrometer range. When PEMFCs are equipped with built-in NiCr/NiSi TFTCs of different hot junction sizes, the time required for the system to reach a steady state varies with the size of the hot junction, with smaller hot junction sizes reaching a steady state more quickly. In a 500-h continuous operation test, the failure rates of NiCr/NiSi TFTCs also vary based on the hot junction size. Both smaller and larger hot junction sizes have relatively higher failure rates, whereas medium-sized junctions have a lower failure rate. These extensive and repetitive comparative experiments provide significant reference value for the size design of TFTCs operating inside PEMFCs, promoting both industrial production and scientific research.
Full article
(This article belongs to the Special Issue Advanced Thin-Films: Design, Fabrication and Applications, 2nd Edition)
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<p>Schematic diagram of the NiCr/NiSi thin-film thermocouple position on the PEM.</p> Full article ">Figure 2
<p>Static and dynamic calibration system structure diagram.</p> Full article ">Figure 3
<p>The effect of different hot point two-dimensional sizes on the response time and temperature measurement accuracy of NiCr/NiSi TFTCs temperature measurement systems.</p> Full article ">Figure 4
<p>(<b>a</b>) The ratio of failed NiCr/NiSi TFTCs to the total number for each hot junction size. (<b>b</b>) The damage location diagram of failed NiCr/NiSi TFTCs.</p> Full article ">Figure 5
<p>Basic characteristics of NiCr and NiSi thin films: (<b>a</b>) XRD patterns of NiCr thin films, NiCr target material, and the standard XRD pattern of Ni<sub>91</sub>Cr<sub>9</sub> (wt.%); XRD patterns of NiSi thermoelectric electrodes, NiSi target material, and the standard XRD pattern of Ni<sub>94</sub>Si<sub>6</sub> (wt.%). (<b>b</b>) EDS spectrum of the NiCr thin film. (<b>c</b>) EDS spectrum of the NiSi thin film.</p> Full article ">Figure 6
<p>The effect of NiCr/NiSi TFTCs with different hot spot sizes on the I-V and I-P polarization curves of PEMFC.</p> Full article ">Figure 7
<p>The calculation results of the benefit coefficient for different junction sizes.</p> Full article ">
<p>Schematic diagram of the NiCr/NiSi thin-film thermocouple position on the PEM.</p> Full article ">Figure 2
<p>Static and dynamic calibration system structure diagram.</p> Full article ">Figure 3
<p>The effect of different hot point two-dimensional sizes on the response time and temperature measurement accuracy of NiCr/NiSi TFTCs temperature measurement systems.</p> Full article ">Figure 4
<p>(<b>a</b>) The ratio of failed NiCr/NiSi TFTCs to the total number for each hot junction size. (<b>b</b>) The damage location diagram of failed NiCr/NiSi TFTCs.</p> Full article ">Figure 5
<p>Basic characteristics of NiCr and NiSi thin films: (<b>a</b>) XRD patterns of NiCr thin films, NiCr target material, and the standard XRD pattern of Ni<sub>91</sub>Cr<sub>9</sub> (wt.%); XRD patterns of NiSi thermoelectric electrodes, NiSi target material, and the standard XRD pattern of Ni<sub>94</sub>Si<sub>6</sub> (wt.%). (<b>b</b>) EDS spectrum of the NiCr thin film. (<b>c</b>) EDS spectrum of the NiSi thin film.</p> Full article ">Figure 6
<p>The effect of NiCr/NiSi TFTCs with different hot spot sizes on the I-V and I-P polarization curves of PEMFC.</p> Full article ">Figure 7
<p>The calculation results of the benefit coefficient for different junction sizes.</p> Full article ">
Open AccessArticle
Open-End Control of Neurite Outgrowth Lengths with Steep Bending Confinement Microchannel Patterns for Miswiring-Free Neuronal Network Formation
by
Naoya Takada, Soya Hagiwara, Nanami Abe, Ryohei Yamazaki, Kazuhiro Tsuneishi and Kenji Yasuda
Micromachines 2024, 15(11), 1374; https://doi.org/10.3390/mi15111374 - 14 Nov 2024
Abstract
Wiring technology to control the length and direction of neurite outgrowth and to connect them is one of the most crucial development issues for forming single-cell-based neuronal networks. However, with current neurite wiring technology, it has been difficult to stop neurite extension at
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Wiring technology to control the length and direction of neurite outgrowth and to connect them is one of the most crucial development issues for forming single-cell-based neuronal networks. However, with current neurite wiring technology, it has been difficult to stop neurite extension at a specific length and connect it to other neurites without causing miswiring due to over-extension. Here, we examined a novel method of wiring neurites without miswiring by controlling the length of neurites in open-ended bending microchannel arrays connected beyond the maximum bending angle of neurite outgrowth. First, we determined the maximum bending angle of neurite elongation to pass through the bending point of a bending microfluidic channel; the maximum angle (the critical angle) was 90°. Next, we confirmed the control of neurite outgrowth length in open-ended microchannels connected at 120°, an angle beyond the maximum bending angle. The neurites stopped when elongated to the bend point, and no further elongation was observed. Finally, we observed that in bending microchannel arrays connected at an angle of 120°, two neurite outgrowths stopped and contacted each other without crossing over the bend point. The results show that the steep bending connection pattern is a robust open-end neurite wiring technique that prevents over-extension and miswiring.
Full article
(This article belongs to the Special Issue Feature Papers of Micromachines in Biology and Biomedicine 2024)
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<p>Fabrication of angled single neurite elongation patterns in agarose microstructures. (<b>A</b>) Schematic drawing of angled agarose microchannels fabrication procedure in a thin agarose layer on the cultivation dish. First, a thin agarose layer is coated on the poly-D-lysine colated cultivation dish. Then, the focused infrared laser is applied to the agarose layer to melt a portion of the agarose for the microchamber and angled microchannel formation. A hippocampal cell is set in the microchamber. Finally, the elongation of neurite in the angled microchannel was observed. (<b>B</b>) Schematic images of the microchamber and angled microchannel for single neurite elongation observation. Left, design of the microchamber and angled microchannel pattern. Right, a neuron is placed in the microchamber, and the elongated neurite is elongated in the angled microchannel. (<b>C</b>) Micrographs of the microchamber and the 45° angled bending microchannel before (<b>left</b>) and after the neurite elongation cultivation started (day 3) (<b>right</b>). Bar, 50 μm.</p> Full article ">Figure 2
<p>Ability of single neurite elongations in various bending angles. (<b>A</b>) Neurite elongations in the various bending microchannels from 20° to 120° bending angles. Neurites in the bending angles larger than 90° stopped their elongation at the bending point. To confirm that they had stopped, two micrographs were compared: left, the day elongation stopped; right, a day after to confirm their stopping. (<b>B</b>) Summary of bending angle dependence of neurite elongations. (<b>C</b>) Observation of the neurite outgrowth until six days (day 8) after its elongation stopped at the 90° bending point (day 2). White arrows indicate the position of the neurite tips. Bars, 50 μm.</p> Full article ">Figure 3
<p>Effect of bending angles for single neurite differentiation. (<b>Left</b>), bending angles. (<b>Middle</b>), phase-contrast images of neurons and their neurites in the microchambers and the bending microchannels. (<b>Right</b>), fluorescent images of immunostained neurons and neurites: neurons and neurites were fixed on day 2 (45°) or day 3 (50° and 90°) after the cultivation started, and were immunostained for Tau-1 to indicate axons (red) and for MAP-2 to indicate cell bodies and dendrites (green). The white arrows show the positions of the elongated neurite tips. Bar, 50 μm.</p> Full article ">Figure 4
<p>Neurite outgrowth length control using angled bending shapes of open-end microtunnel structure. (<b>A</b>,<b>B</b>) Phase-contrast images of neurites elongated from single neurons in the 120° steep bending open-end microchannel series. Microchamber diameter, 20 μm; microchannel lengths, 110 μm (left side), and 30 μm (right side). Bar, 50 μm.</p> Full article ">Figure 5
<p>Connection of length-controlled neurites using 120° steep bending microchannel series. (<b>A</b>,<b>B</b>) Phase-contrast images of neurites elongated from neurons in the 120° steep bending open-end microchannel series. (<b>C</b>) A fluorescent image of immunostained neurons and neurites of (<b>B</b>) neurons and neurites were fixed on day 4 and immunostained for Tau-1 to indicate axons (red) and for MAP-2 to indicate cell bodies and dendrites (green). (<b>D</b>) A magnified fluorescent image of immunostained neurons and neurites of (<b>C</b>) with 40× obj. lens. Bars, 50 μm.</p> Full article ">
<p>Fabrication of angled single neurite elongation patterns in agarose microstructures. (<b>A</b>) Schematic drawing of angled agarose microchannels fabrication procedure in a thin agarose layer on the cultivation dish. First, a thin agarose layer is coated on the poly-D-lysine colated cultivation dish. Then, the focused infrared laser is applied to the agarose layer to melt a portion of the agarose for the microchamber and angled microchannel formation. A hippocampal cell is set in the microchamber. Finally, the elongation of neurite in the angled microchannel was observed. (<b>B</b>) Schematic images of the microchamber and angled microchannel for single neurite elongation observation. Left, design of the microchamber and angled microchannel pattern. Right, a neuron is placed in the microchamber, and the elongated neurite is elongated in the angled microchannel. (<b>C</b>) Micrographs of the microchamber and the 45° angled bending microchannel before (<b>left</b>) and after the neurite elongation cultivation started (day 3) (<b>right</b>). Bar, 50 μm.</p> Full article ">Figure 2
<p>Ability of single neurite elongations in various bending angles. (<b>A</b>) Neurite elongations in the various bending microchannels from 20° to 120° bending angles. Neurites in the bending angles larger than 90° stopped their elongation at the bending point. To confirm that they had stopped, two micrographs were compared: left, the day elongation stopped; right, a day after to confirm their stopping. (<b>B</b>) Summary of bending angle dependence of neurite elongations. (<b>C</b>) Observation of the neurite outgrowth until six days (day 8) after its elongation stopped at the 90° bending point (day 2). White arrows indicate the position of the neurite tips. Bars, 50 μm.</p> Full article ">Figure 3
<p>Effect of bending angles for single neurite differentiation. (<b>Left</b>), bending angles. (<b>Middle</b>), phase-contrast images of neurons and their neurites in the microchambers and the bending microchannels. (<b>Right</b>), fluorescent images of immunostained neurons and neurites: neurons and neurites were fixed on day 2 (45°) or day 3 (50° and 90°) after the cultivation started, and were immunostained for Tau-1 to indicate axons (red) and for MAP-2 to indicate cell bodies and dendrites (green). The white arrows show the positions of the elongated neurite tips. Bar, 50 μm.</p> Full article ">Figure 4
<p>Neurite outgrowth length control using angled bending shapes of open-end microtunnel structure. (<b>A</b>,<b>B</b>) Phase-contrast images of neurites elongated from single neurons in the 120° steep bending open-end microchannel series. Microchamber diameter, 20 μm; microchannel lengths, 110 μm (left side), and 30 μm (right side). Bar, 50 μm.</p> Full article ">Figure 5
<p>Connection of length-controlled neurites using 120° steep bending microchannel series. (<b>A</b>,<b>B</b>) Phase-contrast images of neurites elongated from neurons in the 120° steep bending open-end microchannel series. (<b>C</b>) A fluorescent image of immunostained neurons and neurites of (<b>B</b>) neurons and neurites were fixed on day 4 and immunostained for Tau-1 to indicate axons (red) and for MAP-2 to indicate cell bodies and dendrites (green). (<b>D</b>) A magnified fluorescent image of immunostained neurons and neurites of (<b>C</b>) with 40× obj. lens. Bars, 50 μm.</p> Full article ">
Open AccessArticle
Magnetic Actuation for Wireless Capsule Endoscopy in a Large Workspace Using a Mobile-Coil System
by
Xiao Li, Detian Zeng, Han Xu, Qi Zhang and Bin Liao
Micromachines 2024, 15(11), 1373; https://doi.org/10.3390/mi15111373 - 14 Nov 2024
Abstract
Current wireless capsule endoscopy (WCE) is limited in the long examination time and low flexibility since the capsule is passively moved by the natural peristalsis. Efforts have been made to facilitate the active locomotion of WCE using magnetic actuation and localization technologies. This
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Current wireless capsule endoscopy (WCE) is limited in the long examination time and low flexibility since the capsule is passively moved by the natural peristalsis. Efforts have been made to facilitate the active locomotion of WCE using magnetic actuation and localization technologies. This work focuses on the motion control of the robotic capsule under magnetic actuation in a complex gastrointestinal (GI) tract environment in order to improve the efficiency and accuracy of its motion in dynamic, complex environments. Specifically, a magnetic actuation system based on a four-electromagnetic coil module is designed, and a control strategy for the system is proposed. In particular, the proportional–integral–derivative (PID) control parameters and current values are optimized online and in real time using the adaptive particle swarm optimization (APSO) algorithm. In this paper, both simulations and real-world experiments were conducted using acrylic plates with irregular shapes to simulate the GI tract environment for evaluation. The results demonstrate the potential of the proposed control methods to realize the accurate and efficient inspection of the intestine using active WCE. The methods presented in this paper can be integrated with current WCE to improve the diagnostic accuracy and efficiency of the GI tract.
Full article
(This article belongs to the Topic Micro-Mechatronic Engineering)
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Figure 1
<p>Coil’s structural model whose length and thickness are considered.</p> Full article ">Figure 2
<p>Diagram of the four-electromagnetic coil coordinate system.</p> Full article ">Figure 3
<p>The system’s schematic diagram.</p> Full article ">Figure 4
<p>The curve shows the variation of <math display="inline"><semantics> <mi>ω</mi> </semantics></math> with the iteration number.</p> Full article ">Figure 5
<p>APSO-PID control system’s block diagram.</p> Full article ">Figure 6
<p>Flowchart of APSO algorithm.</p> Full article ">Figure 7
<p>The step response of the magnetic actuation system with different controllers (no disturbance).</p> Full article ">Figure 8
<p>Composite disturbance signal and controller response curves.</p> Full article ">Figure 9
<p>Random disturbance signal and controller response curves.</p> Full article ">Figure 10
<p>Simulation results of the composite disturbance.</p> Full article ">Figure 11
<p>Simulation results for upward step disturbance.</p> Full article ">Figure 12
<p>Steady-state performance of the three trajectories. (<b>a</b>) Error bands and tracking curves for the square trajectory along the X and Y axes; (<b>b</b>) Error bands and tracking curves for the infinity trajectory along the X and Y axes; (<b>c</b>) Error bands and tracking curves for the circular trajectory along the X and Y axes.</p> Full article ">Figure 13
<p>(<b>a</b>) Experimental setup of the magnetic actuation system; (<b>b</b>) inspection routes followed by the WCE during different trajectory inspections, with the WCE position highlighted in red circles.</p> Full article ">Figure 14
<p>Actual experimental results for the three trajectories.</p> Full article ">
<p>Coil’s structural model whose length and thickness are considered.</p> Full article ">Figure 2
<p>Diagram of the four-electromagnetic coil coordinate system.</p> Full article ">Figure 3
<p>The system’s schematic diagram.</p> Full article ">Figure 4
<p>The curve shows the variation of <math display="inline"><semantics> <mi>ω</mi> </semantics></math> with the iteration number.</p> Full article ">Figure 5
<p>APSO-PID control system’s block diagram.</p> Full article ">Figure 6
<p>Flowchart of APSO algorithm.</p> Full article ">Figure 7
<p>The step response of the magnetic actuation system with different controllers (no disturbance).</p> Full article ">Figure 8
<p>Composite disturbance signal and controller response curves.</p> Full article ">Figure 9
<p>Random disturbance signal and controller response curves.</p> Full article ">Figure 10
<p>Simulation results of the composite disturbance.</p> Full article ">Figure 11
<p>Simulation results for upward step disturbance.</p> Full article ">Figure 12
<p>Steady-state performance of the three trajectories. (<b>a</b>) Error bands and tracking curves for the square trajectory along the X and Y axes; (<b>b</b>) Error bands and tracking curves for the infinity trajectory along the X and Y axes; (<b>c</b>) Error bands and tracking curves for the circular trajectory along the X and Y axes.</p> Full article ">Figure 13
<p>(<b>a</b>) Experimental setup of the magnetic actuation system; (<b>b</b>) inspection routes followed by the WCE during different trajectory inspections, with the WCE position highlighted in red circles.</p> Full article ">Figure 14
<p>Actual experimental results for the three trajectories.</p> Full article ">
Open AccessArticle
Denoising Phase-Unwrapped Images in Laser Imaging via Statistical Analysis and DnCNN
by
Yibo Xie, Jin Cheng, Shun Zhou, Qing Fan, Yue Jia, Jingjin Xiao and Weiguo Liu
Micromachines 2024, 15(11), 1372; https://doi.org/10.3390/mi15111372 - 14 Nov 2024
Abstract
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Three-dimensional imaging plays a crucial role at the micro-scale in fields such as precision manufacturing and materials science. However, image noise significantly impacts the accuracy of point cloud reconstruction, making image denoising techniques a widely discussed topic. Statistical analysis of laser imaging noise
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Three-dimensional imaging plays a crucial role at the micro-scale in fields such as precision manufacturing and materials science. However, image noise significantly impacts the accuracy of point cloud reconstruction, making image denoising techniques a widely discussed topic. Statistical analysis of laser imaging noise has led to the conclusion that logarithmically transformed noise follows a Gumbel distribution. A corresponding neural network training set was developed to address the challenges of difficult data collection and the scarcity of phase-unwrapped image datasets. Building on this foundation, a phase-unwrapped image denoising method based on the Denoising Convolutional Neural Network (DnCNN) is proposed. This method aims to achieve three-dimensional filtering by performing two-dimensional image denoising. Experimental results show a significant reduction in the Cloud-to-Mesh Distance (C2M) statistics of the corresponding point clouds before and after planar filtering. Specifically, the statistic at 97.5% of the 2σ principle decreases from 0.8782 mm to 0.3384 mm, highlighting the effectiveness of the filtering algorithm in improving the planar fit. Moreover, the DnCNN method exhibits exceptional denoising performance when applied to real-world target data, such as plaster statues with complex depth variations and PCBs made from different materials, thereby enhancing accuracy and reliability in point cloud reconstruction. This study provides valuable insights into phase-unwrapped image noise suppression in laser imaging, particularly in micro-scale applications where precision is critical.
Full article
Figure 1
Figure 1
<p>Image process of laser imaging reconstruction.</p> Full article ">Figure 2
<p>Schematic of the theoretical analysis of laser imaging.</p> Full article ">Figure 3
<p>Noise distribution of different targets: (<b>a</b>) 3D display of raw data and fitted data for the plane; (<b>b</b>) 3D display of raw data and fitted data for the wall; (<b>c</b>) noise intensity distribution for the plane; (<b>d</b>) noise intensity distribution for the wall.</p> Full article ">Figure 4
<p>Schematic diagram of DnCNN architecture.</p> Full article ">Figure 5
<p>Pixel distribution type of phase unwrapped image.</p> Full article ">Figure 6
<p>Pixel distribution of simulated phase-unwrapped images: (<b>a</b>) phase-unwrapped image of a square target; (<b>b</b>) phase-unwrapped image of a circular target; (<b>c</b>) phase-unwrapped image of a combined square and circular target; (<b>d</b>) local light intensity distribution of the simulated phase-unwrapped images.</p> Full article ">Figure 7
<p>Actual scene of the laser image acquisition system.</p> Full article ">Figure 8
<p>Pixel distribution of the phase-unwrapped image before and after filtering for plane.</p> Full article ">Figure 9
<p>Reconstructed point cloud before and after filtering for plane: (<b>a</b>) point cloud before filtering; (<b>b</b>) point cloud after filtering; (<b>c</b>) ROI point cloud before filtering; (<b>d</b>) ROI point cloud after filtering.</p> Full article ">Figure 10
<p>Cloud-to-Mesh Distance statistics of the point cloud: (<b>a</b>) Cloud-to-Mesh Distance statistics before filtering; (<b>b</b>) Cloud-to-Mesh Distance statistics after filtering.</p> Full article ">Figure 11
<p>Pixel distribution of the phase-unwrapped image before and after filtering for plaster statue.</p> Full article ">Figure 12
<p>Reconstructed point clouds before and after filtering for plaster status: (<b>a</b>) point cloud before filtering; (<b>b</b>) point cloud after filtering; (<b>c</b>) ROI-1 point cloud before filtering; (<b>d</b>) ROI-1 point cloud after filtering; (<b>e</b>) ROI-2 point cloud before filtering; (<b>f</b>) ROI-2 point cloud after filtering.</p> Full article ">Figure 13
<p>Pixel distribution of phase-unwrapped images before and after filtering for PCB.</p> Full article ">Figure 14
<p>Reconstructed point cloud before and after filtering for PCB: (<b>a</b>) point cloud before filtering; (<b>b</b>) point cloud after filtering; (<b>c</b>) ROI-1 point cloud before filtering; (<b>d</b>) ROI-1 point cloud after filtering; (<b>e</b>) ROI-2 point cloud before filtering; (<b>f</b>) ROI-2 point cloud after filtering.</p> Full article ">
<p>Image process of laser imaging reconstruction.</p> Full article ">Figure 2
<p>Schematic of the theoretical analysis of laser imaging.</p> Full article ">Figure 3
<p>Noise distribution of different targets: (<b>a</b>) 3D display of raw data and fitted data for the plane; (<b>b</b>) 3D display of raw data and fitted data for the wall; (<b>c</b>) noise intensity distribution for the plane; (<b>d</b>) noise intensity distribution for the wall.</p> Full article ">Figure 4
<p>Schematic diagram of DnCNN architecture.</p> Full article ">Figure 5
<p>Pixel distribution type of phase unwrapped image.</p> Full article ">Figure 6
<p>Pixel distribution of simulated phase-unwrapped images: (<b>a</b>) phase-unwrapped image of a square target; (<b>b</b>) phase-unwrapped image of a circular target; (<b>c</b>) phase-unwrapped image of a combined square and circular target; (<b>d</b>) local light intensity distribution of the simulated phase-unwrapped images.</p> Full article ">Figure 7
<p>Actual scene of the laser image acquisition system.</p> Full article ">Figure 8
<p>Pixel distribution of the phase-unwrapped image before and after filtering for plane.</p> Full article ">Figure 9
<p>Reconstructed point cloud before and after filtering for plane: (<b>a</b>) point cloud before filtering; (<b>b</b>) point cloud after filtering; (<b>c</b>) ROI point cloud before filtering; (<b>d</b>) ROI point cloud after filtering.</p> Full article ">Figure 10
<p>Cloud-to-Mesh Distance statistics of the point cloud: (<b>a</b>) Cloud-to-Mesh Distance statistics before filtering; (<b>b</b>) Cloud-to-Mesh Distance statistics after filtering.</p> Full article ">Figure 11
<p>Pixel distribution of the phase-unwrapped image before and after filtering for plaster statue.</p> Full article ">Figure 12
<p>Reconstructed point clouds before and after filtering for plaster status: (<b>a</b>) point cloud before filtering; (<b>b</b>) point cloud after filtering; (<b>c</b>) ROI-1 point cloud before filtering; (<b>d</b>) ROI-1 point cloud after filtering; (<b>e</b>) ROI-2 point cloud before filtering; (<b>f</b>) ROI-2 point cloud after filtering.</p> Full article ">Figure 13
<p>Pixel distribution of phase-unwrapped images before and after filtering for PCB.</p> Full article ">Figure 14
<p>Reconstructed point cloud before and after filtering for PCB: (<b>a</b>) point cloud before filtering; (<b>b</b>) point cloud after filtering; (<b>c</b>) ROI-1 point cloud before filtering; (<b>d</b>) ROI-1 point cloud after filtering; (<b>e</b>) ROI-2 point cloud before filtering; (<b>f</b>) ROI-2 point cloud after filtering.</p> Full article ">
Open AccessArticle
Inverse Tesla Valve as Micromixer for Water Purification
by
Christos Liosis, George Sofiadis, Evangelos Karvelas, Theodoros Karakasidis and Ioannis Sarris
Micromachines 2024, 15(11), 1371; https://doi.org/10.3390/mi15111371 - 14 Nov 2024
Abstract
Contaminated water has remained an unsolved problem for decades, particularly when the contamination derived from heavy metals. A possible solution is to mix the contaminated water with magnetic nanoparticles so that an adsorption process can take place. In that frame, Tesla valve micromixer
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Contaminated water has remained an unsolved problem for decades, particularly when the contamination derived from heavy metals. A possible solution is to mix the contaminated water with magnetic nanoparticles so that an adsorption process can take place. In that frame, Tesla valve micromixer and magnetic nanoparticles were selected to perform simulations for encounter maximum mixing efficiency. These simulations focus on inlet velocities ratios between contaminated water and nanoparticles and inlet rates of nanoparticles. The maximum mixing efficiency was 44% for the inverse double Tesla micromixer found for the combination of nanoparticles as the inlet rate and with inlet velocity ratios of .
Full article
(This article belongs to the Special Issue Advanced Micromixing Technology)
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<p>Double Tesla valve geometry.</p> Full article ">Figure 2
<p>Double Tesla valve mesh.</p> Full article ">Figure 3
<p>Velocity magnitude for the double Tesla valve micromixer under various inlet velocity ratios (<b>a</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Streamlines under various inlet velocity ratios (<b>a</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Nanoparticles distribution under various inlet velocity ratios (<b>a</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> with 500 nanoparticles/s inlet rate.</p> Full article ">Figure 6
<p>Mixing efficiency (n) for various inlet velocity ratios and inlet rates of nanoparticles for forward and inverse Tesla valve micromixers.</p> Full article ">
<p>Double Tesla valve geometry.</p> Full article ">Figure 2
<p>Double Tesla valve mesh.</p> Full article ">Figure 3
<p>Velocity magnitude for the double Tesla valve micromixer under various inlet velocity ratios (<b>a</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Streamlines under various inlet velocity ratios (<b>a</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Nanoparticles distribution under various inlet velocity ratios (<b>a</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <msub> <mi>V</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>c</mi> </msub> </mfrac> </mstyle> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> with 500 nanoparticles/s inlet rate.</p> Full article ">Figure 6
<p>Mixing efficiency (n) for various inlet velocity ratios and inlet rates of nanoparticles for forward and inverse Tesla valve micromixers.</p> Full article ">
Open AccessArticle
A Study on Regulating the Residual Stress of Electroplated Cu by Manipulating the Nanotwin Directions
by
Gangli Yang, Tailong Shi, Liu Chang, Hongjia Zhu, Dongyu Tong, Wending Yang, Zeyuan Li and Liyi Li
Micromachines 2024, 15(11), 1370; https://doi.org/10.3390/mi15111370 - 14 Nov 2024
Abstract
Glass substrate, a new type of substrate with excellent mechanical and electrical properties of glass itself, has great potential to become an ideal platform for heterogeneous integration in chiplet systems for high-performance computing applications. The residual stress of the metal layer generated on
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Glass substrate, a new type of substrate with excellent mechanical and electrical properties of glass itself, has great potential to become an ideal platform for heterogeneous integration in chiplet systems for high-performance computing applications. The residual stress of the metal layer generated on the glass surface during the electroplating process is one of the major bottlenecks of glass packaging technologies, resulting in glass-metal layer delamination and glass breakage. This paper demonstrated for the first time a method to regulate the residual stress by manipulating the nanotwin directions of the electroplated Cu. The experimental results show that nanotwins with three different directions (non-directional, vertical, and horizontal) can be manipulated by controlling electroplating conditions (concentration of Cl− and gelatin, stirring speed). The orientations of non-directional, vertical, and horizontal nanotwinned Cu are non-oriented, 110 and 111, respectively. After electroplating, the 111-oriented nanotwinned Cu has the smallest residual stress (39.7 MPa). Annealing can significantly reduce the residual stress of nanotwinned Cu, which has been attributed to the decrease in the geometric necessity dislocation density. 110-oriented nanotwinned Cu had drastic recrystallization, while 111-oriented nanotwinned Cu and non-oriented nanotwinned Cu had only slight recrystallization. After annealing, the residual stress of 111-nt-Cu remains the lowest (29.1 MPa).
Full article
(This article belongs to the Special Issue Two-Dimensional Materials for Electronic and Optoelectronic Devices)
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<p>Microstructure of Ti-Cu seed layer sputtered on glass substrates: (<b>a</b>) FIB image; (<b>b</b>) SEM magnification image of area “A”.</p> Full article ">Figure 2
<p>Cross-sectional FIB images of electroplated Cu and corresponding XRD results: (<b>a1</b>–<b>a3</b>) nt-Cu; (<b>b1</b>–<b>b3</b>) 110-nt-Cu; (<b>c1</b>–<b>c3</b>) 111-nt-Cu. (<b>a1</b>–<b>c1</b>) FIB images; (<b>a2</b>–<b>c2</b>) magnification image; (<b>a3</b>–<b>c3</b>) XRD results; (<b>a4</b>–<b>c4</b>) three-dimensional atomic arrangement model.</p> Full article ">Figure 3
<p>Plan-view EBSD images of electroplated Cu results: (<b>a1</b>,<b>a2</b>) nt-Cu; (<b>b1</b>,<b>b2</b>) 110-nt-Cu; (<b>c1</b>,<b>c2</b>) 111-nt-Cu. (<b>a1</b>–<b>c1</b>) Inverse pole figures; (<b>a2</b>–<b>c2</b>) Pole figures of {111}.</p> Full article ">Figure 4
<p>Residual stress of electroplated Cu with different nanotwin directions.</p> Full article ">Figure 5
<p>FIB and XRD results of electroplated Cu results after annealing at 200 °C for 2 h: (<b>a1</b>,<b>a2</b>) nt-Cu; (<b>b1</b>,<b>b2</b>) 110-nt-Cu; (<b>c1</b>,<b>c2</b>) 111-nt-Cu. (<b>a1</b>–<b>c1</b>) FIB results; (<b>a2</b>–<b>c2</b>) XRD results.</p> Full article ">Figure 6
<p>Plan-view EBSD images of electroplated Cu results after annealing at 200 °C for 2 h: (<b>a1</b>,<b>a2</b>) nt-Cu; (<b>b1</b>,<b>b2</b>) 110-nt-Cu; (<b>c1</b>,<b>c2</b>) 111-nt-Cu. (<b>a1</b>–<b>c1</b>) Inverse pole figures; (<b>a2</b>–<b>c2</b>) Pole figures of {111}.</p> Full article ">Figure 7
<p>Residual stress of electroplated Cu with different nanotwin directions after annealing at 200 °C for 2 h.</p> Full article ">Figure 8
<p>Distribution map of geometric necessity dislocation of electroplated Cu: (<b>a1</b>,<b>a2</b>) nt-Cu; (<b>b1</b>,<b>b2</b>) 110-nt-Cu; (<b>c1</b>,<b>c2</b>) 111-nt-Cu. (<b>a1</b>–<b>c1</b>) Before annealing; (<b>a2</b>–<b>c2</b>) after annealing.</p> Full article ">Figure 9
<p>Geometric necessity dislocation density of electroplated Cu: (<b>a</b>) Before annealing; (<b>b</b>) after annealing.</p> Full article ">
<p>Microstructure of Ti-Cu seed layer sputtered on glass substrates: (<b>a</b>) FIB image; (<b>b</b>) SEM magnification image of area “A”.</p> Full article ">Figure 2
<p>Cross-sectional FIB images of electroplated Cu and corresponding XRD results: (<b>a1</b>–<b>a3</b>) nt-Cu; (<b>b1</b>–<b>b3</b>) 110-nt-Cu; (<b>c1</b>–<b>c3</b>) 111-nt-Cu. (<b>a1</b>–<b>c1</b>) FIB images; (<b>a2</b>–<b>c2</b>) magnification image; (<b>a3</b>–<b>c3</b>) XRD results; (<b>a4</b>–<b>c4</b>) three-dimensional atomic arrangement model.</p> Full article ">Figure 3
<p>Plan-view EBSD images of electroplated Cu results: (<b>a1</b>,<b>a2</b>) nt-Cu; (<b>b1</b>,<b>b2</b>) 110-nt-Cu; (<b>c1</b>,<b>c2</b>) 111-nt-Cu. (<b>a1</b>–<b>c1</b>) Inverse pole figures; (<b>a2</b>–<b>c2</b>) Pole figures of {111}.</p> Full article ">Figure 4
<p>Residual stress of electroplated Cu with different nanotwin directions.</p> Full article ">Figure 5
<p>FIB and XRD results of electroplated Cu results after annealing at 200 °C for 2 h: (<b>a1</b>,<b>a2</b>) nt-Cu; (<b>b1</b>,<b>b2</b>) 110-nt-Cu; (<b>c1</b>,<b>c2</b>) 111-nt-Cu. (<b>a1</b>–<b>c1</b>) FIB results; (<b>a2</b>–<b>c2</b>) XRD results.</p> Full article ">Figure 6
<p>Plan-view EBSD images of electroplated Cu results after annealing at 200 °C for 2 h: (<b>a1</b>,<b>a2</b>) nt-Cu; (<b>b1</b>,<b>b2</b>) 110-nt-Cu; (<b>c1</b>,<b>c2</b>) 111-nt-Cu. (<b>a1</b>–<b>c1</b>) Inverse pole figures; (<b>a2</b>–<b>c2</b>) Pole figures of {111}.</p> Full article ">Figure 7
<p>Residual stress of electroplated Cu with different nanotwin directions after annealing at 200 °C for 2 h.</p> Full article ">Figure 8
<p>Distribution map of geometric necessity dislocation of electroplated Cu: (<b>a1</b>,<b>a2</b>) nt-Cu; (<b>b1</b>,<b>b2</b>) 110-nt-Cu; (<b>c1</b>,<b>c2</b>) 111-nt-Cu. (<b>a1</b>–<b>c1</b>) Before annealing; (<b>a2</b>–<b>c2</b>) after annealing.</p> Full article ">Figure 9
<p>Geometric necessity dislocation density of electroplated Cu: (<b>a</b>) Before annealing; (<b>b</b>) after annealing.</p> Full article ">
Open AccessArticle
The Optical Forces and Torques Exerted by Airy Light-Sheet on Magnetic Particles Utilized for Targeted Drug Delivery
by
Ningning Song, Shiguo Chen, Hao Wang, Xinbo He, Bing Wei, Renxian Li, Shu Zhang and Lei Xu
Micromachines 2024, 15(11), 1369; https://doi.org/10.3390/mi15111369 - 12 Nov 2024
Abstract
The remarkable properties of magnetic nanostructures have sparked considerable interest within the biomedical domain, owing to their potential for diverse applications. In targeted drug delivery systems, therapeutic molecules can be loaded onto magnetic nanocarriers and precisely guided and released within the body with
[...] Read more.
The remarkable properties of magnetic nanostructures have sparked considerable interest within the biomedical domain, owing to their potential for diverse applications. In targeted drug delivery systems, therapeutic molecules can be loaded onto magnetic nanocarriers and precisely guided and released within the body with the assistance of an externally applied magnetic field. However, conventional external magnetic fields generated by permanent magnets or electromagnets are limited by finite magnetic field gradients, shallow penetration depths, and low precision. The novel structured light field known as the Airy light-sheet possesses unique characteristics such as non-diffraction, self-healing, and self-acceleration, which can potentially overcome the limitations of traditional magnetic fields to some extent. While existing studies have primarily focused on the manipulation of dielectric particles by Airy light-sheet, comprehensive analyses exploring the intricate interplay between Airy light-sheet and magnetic nanostructures are currently lacking in the literature, with only preliminary theoretical discussions available. This study systematically explores the mechanical response of magnetic spherical particles under the influence of Airy light-sheet, including radiation forces and spin torques. Furthermore, we provide an in-depth analysis of the effects of particle size, permittivity, permeability, and incident light-sheet parameters on the mechanical effects. Our research findings not only offer new theoretical guidance and practical references for the application of magnetic nanoparticles in biomedicine but also provide valuable insights for the manipulation of other types of micro/nanoparticles using structured light fields.
Full article
(This article belongs to the Section B5: Drug Delivery System)
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Show Figures
Figure 1
Figure 1
<p>Schematic illustration of Airy light-sheet propagation in space. (<b>a</b>) The definition of the wave vector <math display="inline"><semantics> <mover accent="true"> <mi>k</mi> <mo>→</mo> </mover> </semantics></math>, the coordinate vector <math display="inline"><semantics> <mover accent="true"> <mi>r</mi> <mo>→</mo> </mover> </semantics></math>, and its angle with the coordinate axis. (<b>b</b>) Schematic representation of the force and torque acting on magnetic spherical particles in an Airy light field.</p> Full article ">Figure 2
<p>Comparison of optical force on dielectric (<math display="inline"><semantics> <msub> <mi>μ</mi> <mi>r</mi> </msub> </semantics></math> = 1) and magnetic (<math display="inline"><semantics> <msub> <mi>μ</mi> <mi>r</mi> </msub> </semantics></math> = 1000) spheres.</p> Full article ">Figure 3
<p>The influence of the transverse scale parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <msub> <mi>ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math> (ranging from 0 to 35) of the Airy light-sheet and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) of the sphere on the optical radiation forces.</p> Full article ">Figure 4
<p>The influence of the beam attenuation parameter <math display="inline"><semantics> <mi>γ</mi> </semantics></math> (ranging from 0 to 0.4) of the Airy light-sheet and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) of the sphere on the optical radiation forces.</p> Full article ">Figure 5
<p>The influence of the real part of the relative permittivity of magnetic spherical particles (ranging from 0 to 8) and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) on the optical radiation forces.</p> Full article ">Figure 6
<p>The influence of the imaginary part of the relative permittivity of magnetic spherical particles (ranging from 0 to 8) and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) on the optical radiation forces.</p> Full article ">Figure 7
<p>The influence of the real part of the relative permeability of magnetic spherical particles (ranging from 0 to 1000) and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) on the optical radiation forces.</p> Full article ">Figure 8
<p>The influence of the imaginary part of the relative permeability of magnetic spherical particles (ranging from 0 to 8) and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) on the optical radiation forces.</p> Full article ">Figure 9
<p>Two-dimensional spatial distribution of optical radiation forces exerted by an Airy light-sheet on a magnetic spherical particle (particle radius = 10 nm).</p> Full article ">Figure 10
<p>The same as in <a href="#micromachines-15-01369-f009" class="html-fig">Figure 9</a> but magnetic spherical particle radius is 100 nm.</p> Full article ">Figure 11
<p>The same as in <a href="#micromachines-15-01369-f009" class="html-fig">Figure 9</a> but magnetic spherical particle radius is 200 nm.</p> Full article ">Figure 12
<p>The same as in <a href="#micromachines-15-01369-f009" class="html-fig">Figure 9</a> but set the refractive index of the environment to 1.45.</p> Full article ">Figure 13
<p>The same as in <a href="#micromachines-15-01369-f010" class="html-fig">Figure 10</a> but set the refractive index of the environment to 1.45.</p> Full article ">Figure 13 Cont.
<p>The same as in <a href="#micromachines-15-01369-f010" class="html-fig">Figure 10</a> but set the refractive index of the environment to 1.45.</p> Full article ">Figure 14
<p>The same as in <a href="#micromachines-15-01369-f011" class="html-fig">Figure 11</a> but set the refractive index of the environment to 1.45.</p> Full article ">Figure 15
<p>Comparison of OST on dielectric (<math display="inline"><semantics> <msub> <mi>μ</mi> <mi>r</mi> </msub> </semantics></math> = 1) and magnetic (<math display="inline"><semantics> <msub> <mi>μ</mi> <mi>r</mi> </msub> </semantics></math> = 1000) spheres.</p> Full article ">Figure 16
<p>The influence of the incident Airy light-sheet parameters on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math> (N·m). (<b>a</b>) The effects of <math display="inline"><semantics> <mrow> <mi>k</mi> <msub> <mi>ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>. (<b>b</b>) The effects of <math display="inline"><semantics> <mi>γ</mi> </semantics></math> on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>.</p> Full article ">Figure 17
<p>The influence of the relative permittivity of magnetic spherical particles on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math> (N·m). (<b>a</b>) The effects of the real part of the relative permittivity on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>. (<b>b</b>) The effects of the imaginary part of the relative permittivity on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>.</p> Full article ">Figure 18
<p>The influence of the relative permeability of magnetic spherical particles on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math> (N·m). (<b>a</b>) The effects of the real part of the relative permeability on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>. (<b>b</b>) The effects of the imaginary part of the relative permeability on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>.</p> Full article ">Figure 19
<p>Two-dimensional spatial distribution of torque component <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math> (N.m) exerted by an Airy light-sheet on a magnetic spherical particle. (<b>a</b>) The magnetic spherical particle radius is 10 nm. (<b>b</b>) The magnetic spherical particle radius is 100 nm. (<b>c</b>) The magnetic spherical particle radius is 200 nm.</p> Full article ">Figure 20
<p>The same as in <a href="#micromachines-15-01369-f019" class="html-fig">Figure 19</a> but set the refractive index of the environment to 1.45.</p> Full article ">
<p>Schematic illustration of Airy light-sheet propagation in space. (<b>a</b>) The definition of the wave vector <math display="inline"><semantics> <mover accent="true"> <mi>k</mi> <mo>→</mo> </mover> </semantics></math>, the coordinate vector <math display="inline"><semantics> <mover accent="true"> <mi>r</mi> <mo>→</mo> </mover> </semantics></math>, and its angle with the coordinate axis. (<b>b</b>) Schematic representation of the force and torque acting on magnetic spherical particles in an Airy light field.</p> Full article ">Figure 2
<p>Comparison of optical force on dielectric (<math display="inline"><semantics> <msub> <mi>μ</mi> <mi>r</mi> </msub> </semantics></math> = 1) and magnetic (<math display="inline"><semantics> <msub> <mi>μ</mi> <mi>r</mi> </msub> </semantics></math> = 1000) spheres.</p> Full article ">Figure 3
<p>The influence of the transverse scale parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <msub> <mi>ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math> (ranging from 0 to 35) of the Airy light-sheet and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) of the sphere on the optical radiation forces.</p> Full article ">Figure 4
<p>The influence of the beam attenuation parameter <math display="inline"><semantics> <mi>γ</mi> </semantics></math> (ranging from 0 to 0.4) of the Airy light-sheet and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) of the sphere on the optical radiation forces.</p> Full article ">Figure 5
<p>The influence of the real part of the relative permittivity of magnetic spherical particles (ranging from 0 to 8) and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) on the optical radiation forces.</p> Full article ">Figure 6
<p>The influence of the imaginary part of the relative permittivity of magnetic spherical particles (ranging from 0 to 8) and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) on the optical radiation forces.</p> Full article ">Figure 7
<p>The influence of the real part of the relative permeability of magnetic spherical particles (ranging from 0 to 1000) and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) on the optical radiation forces.</p> Full article ">Figure 8
<p>The influence of the imaginary part of the relative permeability of magnetic spherical particles (ranging from 0 to 8) and the optical size parameter <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>a</mi> </mrow> </semantics></math> (ranging from 0 to 12) on the optical radiation forces.</p> Full article ">Figure 9
<p>Two-dimensional spatial distribution of optical radiation forces exerted by an Airy light-sheet on a magnetic spherical particle (particle radius = 10 nm).</p> Full article ">Figure 10
<p>The same as in <a href="#micromachines-15-01369-f009" class="html-fig">Figure 9</a> but magnetic spherical particle radius is 100 nm.</p> Full article ">Figure 11
<p>The same as in <a href="#micromachines-15-01369-f009" class="html-fig">Figure 9</a> but magnetic spherical particle radius is 200 nm.</p> Full article ">Figure 12
<p>The same as in <a href="#micromachines-15-01369-f009" class="html-fig">Figure 9</a> but set the refractive index of the environment to 1.45.</p> Full article ">Figure 13
<p>The same as in <a href="#micromachines-15-01369-f010" class="html-fig">Figure 10</a> but set the refractive index of the environment to 1.45.</p> Full article ">Figure 13 Cont.
<p>The same as in <a href="#micromachines-15-01369-f010" class="html-fig">Figure 10</a> but set the refractive index of the environment to 1.45.</p> Full article ">Figure 14
<p>The same as in <a href="#micromachines-15-01369-f011" class="html-fig">Figure 11</a> but set the refractive index of the environment to 1.45.</p> Full article ">Figure 15
<p>Comparison of OST on dielectric (<math display="inline"><semantics> <msub> <mi>μ</mi> <mi>r</mi> </msub> </semantics></math> = 1) and magnetic (<math display="inline"><semantics> <msub> <mi>μ</mi> <mi>r</mi> </msub> </semantics></math> = 1000) spheres.</p> Full article ">Figure 16
<p>The influence of the incident Airy light-sheet parameters on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math> (N·m). (<b>a</b>) The effects of <math display="inline"><semantics> <mrow> <mi>k</mi> <msub> <mi>ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math> on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>. (<b>b</b>) The effects of <math display="inline"><semantics> <mi>γ</mi> </semantics></math> on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>.</p> Full article ">Figure 17
<p>The influence of the relative permittivity of magnetic spherical particles on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math> (N·m). (<b>a</b>) The effects of the real part of the relative permittivity on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>. (<b>b</b>) The effects of the imaginary part of the relative permittivity on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>.</p> Full article ">Figure 18
<p>The influence of the relative permeability of magnetic spherical particles on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math> (N·m). (<b>a</b>) The effects of the real part of the relative permeability on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>. (<b>b</b>) The effects of the imaginary part of the relative permeability on <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math>.</p> Full article ">Figure 19
<p>Two-dimensional spatial distribution of torque component <math display="inline"><semantics> <msub> <mi>T</mi> <mi>x</mi> </msub> </semantics></math> (N.m) exerted by an Airy light-sheet on a magnetic spherical particle. (<b>a</b>) The magnetic spherical particle radius is 10 nm. (<b>b</b>) The magnetic spherical particle radius is 100 nm. (<b>c</b>) The magnetic spherical particle radius is 200 nm.</p> Full article ">Figure 20
<p>The same as in <a href="#micromachines-15-01369-f019" class="html-fig">Figure 19</a> but set the refractive index of the environment to 1.45.</p> Full article ">
Open AccessReview
Neural Network Methods in the Development of MEMS Sensors
by
Yan Liu, Mingda Ping, Jizhou Han, Xiang Cheng, Hongbo Qin and Weidong Wang
Micromachines 2024, 15(11), 1368; https://doi.org/10.3390/mi15111368 - 12 Nov 2024
Abstract
As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization,
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As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area.
Full article
(This article belongs to the Special Issue MEMS/NEMS Sensors and Actuators, 2nd Edition)
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Show Figures
Figure 1
Figure 1
<p>Overview of a single iteration in the development of MEMS sensors with conventional and NN methods.</p> Full article ">Figure 2
<p>The disk MEMS resonator for dimensional optimization with NN method: (<b>a</b>) the structural diagram (<b>up</b>) and target dimensions (<b>down</b>), (<b>b</b>) the architecture and operating process of simulation analyzer based on MLPNN. Reproduced under the terms and conditions of the Creative Commons Attribution license of [<a href="#B55-micromachines-15-01368" class="html-bibr">55</a>].</p> Full article ">Figure 3
<p>Flowchart of the optimization strategy using NN-based correlation models.</p> Full article ">Figure 4
<p>Sketch for the tandem network constructed by inverse network (IN) and pre-trained forward network.</p> Full article ">Figure 5
<p>The structure for the ResNet model in predicting performances of resonator: (<b>a</b>) 100 × 100 pixelated binary 2D image to present the resonator; (<b>b</b>) structure of the ResNet model. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B71-micromachines-15-01368" class="html-bibr">71</a>].</p> Full article ">Figure 6
<p>The image dataset for the defect detection in pressure sensors: (<b>a</b>) five kinds of defect images; (<b>b</b>) the data from augmentation. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B82-micromachines-15-01368" class="html-bibr">82</a>].</p> Full article ">Figure 7
<p>The two-scale multi-physics deep learning model for MEMS magnetometer: (<b>a</b>) Example of parent SVE, its instances, and the resonant structure; (<b>b</b>) architectures of the model used for the device-level mapping. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B95-micromachines-15-01368" class="html-bibr">95</a>].</p> Full article ">Figure 8
<p>BPNN in the temperature compensation of MEMS sensors: (<b>a</b>) basic BPNN, (<b>b</b>) procedure for using BPNN, and (<b>c</b>) procedure for using GA-BP. Reused with the permissions from [<a href="#B106-micromachines-15-01368" class="html-bibr">106</a>,<a href="#B108-micromachines-15-01368" class="html-bibr">108</a>].</p> Full article ">Figure 9
<p>LSTM in the temperature compensation of MEMS sensors: (<b>a</b>) basic LSTM, (<b>b</b>) varied DLSTM, and (<b>c</b>) procedure for using ISSA-LSTM. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B121-micromachines-15-01368" class="html-bibr">121</a>].</p> Full article ">Figure 10
<p>Calib-Net for low-cost IMU calibration: (<b>a</b>) the overview of calibration process; (<b>b</b>) calibration performance evaluation using VIO methods. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B139-micromachines-15-01368" class="html-bibr">139</a>].</p> Full article ">Figure 11
<p>The learning framework of using the LSTM model in human glucose monitoring. Reused with the permission from [<a href="#B144-micromachines-15-01368" class="html-bibr">144</a>].</p> Full article ">Figure 12
<p>Using LeNet-5 for gas identification: the responses of 12 sensors to CH4 (<b>a</b>), CO (<b>b</b>), and CH4 + CO (<b>c</b>); (<b>d</b>) the proposed LeNet-5 structure for gas identification; (<b>e</b>) patterns of downsampled matrixes used in LeNet-5 for different gases (unit: ppm). Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B151-micromachines-15-01368" class="html-bibr">151</a>].</p> Full article ">Figure 13
<p>Flow of the gas recognition with SMMA for denoising, GA for data extraction, and BPNN for recognition. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B153-micromachines-15-01368" class="html-bibr">153</a>].</p> Full article ">Figure 14
<p>Block diagram illustrating the sensing approach that combines physics-based modeling and deep learning. Reused with the permission from [<a href="#B157-micromachines-15-01368" class="html-bibr">157</a>].</p> Full article ">
<p>Overview of a single iteration in the development of MEMS sensors with conventional and NN methods.</p> Full article ">Figure 2
<p>The disk MEMS resonator for dimensional optimization with NN method: (<b>a</b>) the structural diagram (<b>up</b>) and target dimensions (<b>down</b>), (<b>b</b>) the architecture and operating process of simulation analyzer based on MLPNN. Reproduced under the terms and conditions of the Creative Commons Attribution license of [<a href="#B55-micromachines-15-01368" class="html-bibr">55</a>].</p> Full article ">Figure 3
<p>Flowchart of the optimization strategy using NN-based correlation models.</p> Full article ">Figure 4
<p>Sketch for the tandem network constructed by inverse network (IN) and pre-trained forward network.</p> Full article ">Figure 5
<p>The structure for the ResNet model in predicting performances of resonator: (<b>a</b>) 100 × 100 pixelated binary 2D image to present the resonator; (<b>b</b>) structure of the ResNet model. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B71-micromachines-15-01368" class="html-bibr">71</a>].</p> Full article ">Figure 6
<p>The image dataset for the defect detection in pressure sensors: (<b>a</b>) five kinds of defect images; (<b>b</b>) the data from augmentation. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B82-micromachines-15-01368" class="html-bibr">82</a>].</p> Full article ">Figure 7
<p>The two-scale multi-physics deep learning model for MEMS magnetometer: (<b>a</b>) Example of parent SVE, its instances, and the resonant structure; (<b>b</b>) architectures of the model used for the device-level mapping. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B95-micromachines-15-01368" class="html-bibr">95</a>].</p> Full article ">Figure 8
<p>BPNN in the temperature compensation of MEMS sensors: (<b>a</b>) basic BPNN, (<b>b</b>) procedure for using BPNN, and (<b>c</b>) procedure for using GA-BP. Reused with the permissions from [<a href="#B106-micromachines-15-01368" class="html-bibr">106</a>,<a href="#B108-micromachines-15-01368" class="html-bibr">108</a>].</p> Full article ">Figure 9
<p>LSTM in the temperature compensation of MEMS sensors: (<b>a</b>) basic LSTM, (<b>b</b>) varied DLSTM, and (<b>c</b>) procedure for using ISSA-LSTM. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B121-micromachines-15-01368" class="html-bibr">121</a>].</p> Full article ">Figure 10
<p>Calib-Net for low-cost IMU calibration: (<b>a</b>) the overview of calibration process; (<b>b</b>) calibration performance evaluation using VIO methods. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B139-micromachines-15-01368" class="html-bibr">139</a>].</p> Full article ">Figure 11
<p>The learning framework of using the LSTM model in human glucose monitoring. Reused with the permission from [<a href="#B144-micromachines-15-01368" class="html-bibr">144</a>].</p> Full article ">Figure 12
<p>Using LeNet-5 for gas identification: the responses of 12 sensors to CH4 (<b>a</b>), CO (<b>b</b>), and CH4 + CO (<b>c</b>); (<b>d</b>) the proposed LeNet-5 structure for gas identification; (<b>e</b>) patterns of downsampled matrixes used in LeNet-5 for different gases (unit: ppm). Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B151-micromachines-15-01368" class="html-bibr">151</a>].</p> Full article ">Figure 13
<p>Flow of the gas recognition with SMMA for denoising, GA for data extraction, and BPNN for recognition. Reproduced under the terms and conditions of the Creative Commons Attribution License of [<a href="#B153-micromachines-15-01368" class="html-bibr">153</a>].</p> Full article ">Figure 14
<p>Block diagram illustrating the sensing approach that combines physics-based modeling and deep learning. Reused with the permission from [<a href="#B157-micromachines-15-01368" class="html-bibr">157</a>].</p> Full article ">
Open AccessArticle
A Laterally Excited Bulk Acoustic Wave Resonator Based on LiNbO3 with Arc-Shaped Electrodes
by
Jieyu Liu, Wenjuan Liu, Zhiwei Wen, Min Zeng and Chengliang Sun
Micromachines 2024, 15(11), 1367; https://doi.org/10.3390/mi15111367 - 12 Nov 2024
Abstract
High frequency and large bandwidth are growing trends in communication radio-frequency devices. The LiNbO3 thin film material is expected to become the preferred piezoelectric material for high coupling resonators in the 5G frequency band due to its ultra-high piezoelectric coefficient and low
[...] Read more.
High frequency and large bandwidth are growing trends in communication radio-frequency devices. The LiNbO3 thin film material is expected to become the preferred piezoelectric material for high coupling resonators in the 5G frequency band due to its ultra-high piezoelectric coefficient and low loss characteristics. The main mode of laterally excited bulk acoustic wave resonators (XBAR) have an ultra-high sound velocity, which enables high-frequency applications. However, the interference of spurious modes is one of the main reasons hindering the widespread application of XBAR. In this paper, a Z-cut LiNbO3 thin film-based XBAR with arc-shaped electrodes is presented. We investigate the electric field distribution of the XBAR, while the irregular boundary of the arc-shaped electrodes affects the electric field between the existing interdigital transducers (IDTs). The mode shapes and impedance response of the XBAR with arc-shaped electrodes and the XBARs with traditional IDTs are compared in this work. The fabricated XBAR on a 350 nm Z-cut LiNbO3 thin film with arc-shaped electrodes operating at over 5 GHz achieves a high effective electromechanical coupling coefficient of 29.8% and the spurious modes are well suppressed. This work promotes an XBAR with an optimized electrode design to further achieve the desired performance.
Full article
(This article belongs to the Special Issue Piezoelectric Devices and System in Micromachines)
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Show Figures
Figure 1
Figure 1
<p>Two-dimensional cross-sectional view of XBARs (<b>a</b>) with IDT electrodes and (<b>b</b>) with arc-shaped electrodes. (<b>c</b>) The cross-sectional view of a XBAR based on a Z-cut LiNbO<sub>3</sub> thin film.</p> Full article ">Figure 2
<p>(<b>a</b>) The schematic of hexagonal unit cell of Z-cut LiNbO<sub>3</sub>. Dispersive diagrams for propagation on the x-axis for the real part of propagation constant (<b>b</b>) while the frequency domain is 0.1–3.5 GHz, (<b>c</b>) while the frequency domain is 3.5–5.5 GHz and (<b>d</b>) while the frequency domain is 5.5–6.5 GHz. (<b>e</b>) The total displacements of different mode shapes while kx = 0 (1/m).</p> Full article ">Figure 3
<p>Process flows of XBAR: (<b>a</b>) The schematic diagram of LNOI wafer. (<b>b</b>) Definition of top electrode. (<b>c</b>) DRIE back surface Si. (<b>d</b>) Suspend the resonator using BOE.</p> Full article ">Figure 4
<p>The microscope images of (<b>a</b>) device with IDT electrodes and (<b>b</b>) device with arc-shaped electrodes.</p> Full article ">Figure 5
<p>The SEM pictures of the fabricated XBARs (<b>a</b>) with arc-shaped electrodes and (<b>b</b>) with IDT electrodes. Measured impedance responses of XBARs (<b>c</b>) with arc-shaped electrodes and (<b>d</b>) with IDT electrodes.</p> Full article ">Figure 6
<p>Simulated impedance curve of XBARs (<b>a</b>) with IDT electrodes and (<b>b</b>) with arc-shaped electrodes.</p> Full article ">Figure 7
<p>Schematic diagram of acoustic wave propagation between adjacent (<b>a</b>) IDT electrodes and (<b>b</b>) arc-shaped electrodes. Simulated electric field distributions of XBARs (<b>c</b>) with IDT electrodes and (<b>d</b>) with arc-shaped electrodes.</p> Full article ">Figure 8
<p>Impedance curve of arc-shaped electrode XBAR when a = 10 μm and (<b>a</b>) b = 2 μm; (<b>b</b>) b = 5 μm; (<b>c</b>) b = 7.5 μm; (<b>d</b>) b = 10 μm.</p> Full article ">
<p>Two-dimensional cross-sectional view of XBARs (<b>a</b>) with IDT electrodes and (<b>b</b>) with arc-shaped electrodes. (<b>c</b>) The cross-sectional view of a XBAR based on a Z-cut LiNbO<sub>3</sub> thin film.</p> Full article ">Figure 2
<p>(<b>a</b>) The schematic of hexagonal unit cell of Z-cut LiNbO<sub>3</sub>. Dispersive diagrams for propagation on the x-axis for the real part of propagation constant (<b>b</b>) while the frequency domain is 0.1–3.5 GHz, (<b>c</b>) while the frequency domain is 3.5–5.5 GHz and (<b>d</b>) while the frequency domain is 5.5–6.5 GHz. (<b>e</b>) The total displacements of different mode shapes while kx = 0 (1/m).</p> Full article ">Figure 3
<p>Process flows of XBAR: (<b>a</b>) The schematic diagram of LNOI wafer. (<b>b</b>) Definition of top electrode. (<b>c</b>) DRIE back surface Si. (<b>d</b>) Suspend the resonator using BOE.</p> Full article ">Figure 4
<p>The microscope images of (<b>a</b>) device with IDT electrodes and (<b>b</b>) device with arc-shaped electrodes.</p> Full article ">Figure 5
<p>The SEM pictures of the fabricated XBARs (<b>a</b>) with arc-shaped electrodes and (<b>b</b>) with IDT electrodes. Measured impedance responses of XBARs (<b>c</b>) with arc-shaped electrodes and (<b>d</b>) with IDT electrodes.</p> Full article ">Figure 6
<p>Simulated impedance curve of XBARs (<b>a</b>) with IDT electrodes and (<b>b</b>) with arc-shaped electrodes.</p> Full article ">Figure 7
<p>Schematic diagram of acoustic wave propagation between adjacent (<b>a</b>) IDT electrodes and (<b>b</b>) arc-shaped electrodes. Simulated electric field distributions of XBARs (<b>c</b>) with IDT electrodes and (<b>d</b>) with arc-shaped electrodes.</p> Full article ">Figure 8
<p>Impedance curve of arc-shaped electrode XBAR when a = 10 μm and (<b>a</b>) b = 2 μm; (<b>b</b>) b = 5 μm; (<b>c</b>) b = 7.5 μm; (<b>d</b>) b = 10 μm.</p> Full article ">
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