When Device Modeling Meets Machine Learning: Opportunities and Challenges (Invited)
Article No.: 36, Pages 1 - 6
Abstract
Device modeling is essential for circuit simulations and designs in terms of constructing the circuit matrix equations of KCL and KVL. While there are classical methodologies, machine learning techniques are promising to bring innovations in the landscape of device modeling. This work reviews the device modeling from a top-down perspective, covering two different interpretations of modeling. Then the recent process in the domain-specific machine learning approaches is briefly summarized for logic and memory devices. The challenges ahead, for the machine learning model to support the industry's practical needs, are analyzed. A concept of fusion model, by deeply merging device physics and neural networks, is also explained.
References
[1]
M. Rapp, H. Armouch, Y. Lin, B. Yu, D. Z. Pan, M. Wolf, J. Henkel, "MLCAD: a survey of research in machine learning for CAD," IEEE TCAD, vol. 41, no. 10, pp. 3162--3181, 2022.
[2]
F. Klemme, J. Prinz, V. M. van Santen, J. Henkel, and H. Amrouch, "Modeling emerging technologies using machine learning: Challenges and opportunities", in Proc. ICCAD, 2020, pp. 1--9.
[3]
L. Chen, et al., "The dawn of AI-native EDA: promises and challenges of large circuit models," arXiv preprint, arXiv:2403.07257, 2024.
[4]
Semikong, http://semikong.aitomatic.com/, accessed on July, 2024
[5]
J. R. Brews, "A charge-sheet model of the MOSFET," Solid-State Electronics, vol. 21, no. 2, pp. 345--355, 1978.
[6]
L. Zhang, M. Chan, " Artificial neural network design for compact modeling of generic transistors," J. Comput. Electron., vol. 16, no. 3, pp. 825--832, Sep. 2017.
[7]
J. Wang, et al., "Artificial Neural Network-Based Compact Modeling Methodology for Advanced Transistors, " IEEE Trans. Electron Devices, vol. 68, no. 3, pp. 1318--1325, March 2021.
[8]
C. T. Tung, C. Hu, "Neural Network-Based BSIM Transistor Model Framework: Currents, Charges, Variability, and Circuit Simulation," IEEE Trans. Electron Devices, vol. 70, no. 4, pp. 2157--2160, 2023.
[9]
W. Dai, et al., "An Automatic Integration Network Approach for Generic Device Charge Modeling," in Proc. ICSICT, 2022, pp. 1--3.
[10]
J. Choi, et al., "Enhancement and expansion of the neural network-based compact model using a binning method," IEEE JEDS, vol, 12, pp. 65--73, 2024.
[11]
Y. Li, W. Dai, K. Geng, L. Zhang, R. Wang, R. Huang, "An Automatic Parameter Extraction Method Based on Autoencoder for PIN Diode Model," in Proc. ICSICT, 2022, pp. 1--3.
[12]
M. Kao, F. Chavez, S. Khandelwal and C. Hu, "Deep Learning-Based BSIM-CMG Parameter Extraction for 10-nm FinFET," IEEE Trans. Electron Devices, vol. 69, no. 8, pp. 4765--4768, 2022.
[13]
F. Chavez, C. T. Tung, M. Yao, C. Hu, J. Chen, S. Khandelwal, "Deep learnmg-based I-V global parameter extraction for BSIM-CMG," Solid-State Electronics, 209, pp. 108766, 2023.
[14]
A. Ashai, et al., "Deep learning-based fast BSIM-CMG parameter extraction for general input dataset," IEEE Trans. Electron Devices, vol. 70, no. 7, pp. 3437--3441, 2023.
[15]
J. Dobeš, L. Pospíšil and A. Yadav, "Precise characterization of memristive systems by cooperative artificial neural networks," in Proc. SCIS-ISIS, 2012, pp. 2130--2133.
[16]
A. Lin, et al., "A process-aware memory compact-device model using long-short term memory," IEEE Access, vol. 9, pp. 3126--3139, 2021.
[17]
Y. Zhang, et al., "GEM: a generalized memristor device modeling framework based on neural network for transient circuit simulation," IEEE TCAD, vol. 42, no. 3, pp. 834--846, 2023.
[18]
Z. Rong, W. Dai, L. Zhang and M. Chan, "Generic Compact Modeling of Emerging Memories with Recurrent NARX Network," IEEE Electron Device Letters, vol. 44, no. 8, pp. 1272--1275, 2023.
[19]
Z. Zhang, et al., "New-Generation Design-Technology Co-Optimization (DTCO): Mechaine-Learning Assisted Modeling Framework," in Proc. SWW, 2019, pp. 1--2.
[20]
W. Dai, et al., "Benchmarking Artificial Neural Network Models for Design Technology Co-optimization," in Proc. ISEDA, 2023, pp. 423--427.
[21]
W. Dai, et al., "Statistical compact modeling with artificial neural networks," IEEE TCAD, vol. 42, no. 12, pp. 5156--5160, 2023.
[22]
C. Park, H. Cho, J. Lee, "Enhancing interpretability of neural compact mdoels: towards reliable device modeling," IEEE JEDS, early access.
[23]
H. Liu, et al., "A neural network-based framework for accelerated device-circuit electrothermal co-simulations in GAAFETs," in Proc. ISEDA, 2024, pp. 1--5.
[24]
Z. Yang, A. D. Gaidhane, K. Anderson, G. Workman and Y. Cao, "Graph-Based Compact Model (GCM) for Efficient Transistor Parameter Extraction: A Machine Learning Approach on 12 nm FinFETs," IEEE Trans. Electron Devices, vol. 71, no. 1, pp. 254--262, 2024.
Index Terms
- When Device Modeling Meets Machine Learning: Opportunities and Challenges (Invited)
Recommendations
Machine Learning: The State of the Art
The two fundamental problems in machine learning (ML) are statistical analysis and algorithm design. The former tells us the principles of the mathematical models that we establish from the observation data. The latter defines the conditions on which ...
Machine learning towards intelligent systems: applications, challenges, and opportunities
AbstractThe emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand ...
Machine learning based learner modeling for adaptive web-based learning
ICCSA'07: Proceedings of the 2007 international conference on Computational science and its applications - Volume Part IEspecially in the first decade of this century, learner adapted interaction and learner modeling are becoming more important in the area of web-based learning systems. The complicated nature of the problem is a serious challenge with vast amount of data ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
September 2024
321 pages
ISBN:9798400706998
DOI:10.1145/3670474
- General Chairs:
- Hussam Amrouch,
- Jiang Hu,
- Program Chairs:
- Siddharth Garg,
- Yibo Lin
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 September 2024
Check for updates
Author Tags
Qualifiers
- Invited-talk
- Research
- Refereed limited
Conference
MLCAD '24
Sponsor:
MLCAD '24: 2024 ACM/IEEE International Symposium on Machine Learning for CAD
September 9 - 11, 2024
UT, Salt Lake City, USA
Acceptance Rates
MLCAD '24 Paper Acceptance Rate 35 of 83 submissions, 42%;
Overall Acceptance Rate 35 of 83 submissions, 42%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 83Total Downloads
- Downloads (Last 12 months)83
- Downloads (Last 6 weeks)17
Reflects downloads up to 14 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in