Enhancing the Capabilities of Quantum Transport Simulations Utilizing Machine Learning Strategies
Authors: 
Ateeb Naseer, Yawar Hayat Zarkob, Musaib Rafiq, Mohammad Sajid Nazir, Owais Ahmad, Amit Agarwal, Somnath Bhowmick, Yogesh Singh ChauhanAuthors Info & Claims
Article No.: 5, Pages 1 - 7
Abstract
The ongoing pursuit of exploring novel materials for potential future device applications continues to strengthen the vital role of Technology Computer-Aided Design (TCAD) simulations within the device community. However, its computationally intensive and time-consuming nature necessitates novel methodologies to overcome the limitations. Machine learning (ML) is a potential remedy in our contemporary data-driven society. In this work, we present a unique approach based on Neural Networks (NNs) to generate an efficient potential profile guess. The predicted ML potential enhances the convergence of the coupled Schrodinger and Poisson equation and speeds up the simulation process, ~ 2.5x, by reducing the number of self-consistent iterations at each bias point. Moreover, our method demonstrates versatility by providing reasonable prediction capacity and accuracy for grid reduction, doping, and channel material variations.
References
[1]
Saptarshi Das, Richard Gulotty, Anirudha V. Sumant, and Andreas Roelofs. All two-dimensional, flexible, transparent, and thinnest thin film transistor. Nano Letters, 14(5):2861--2866, 2014.
[2]
Keshari Nandan, Amit Agarwal, Somnath Bhowmick, and Yogesh S. Chauhan. Two-dimensional semiconductors based field-effect transistors: review of major milestones and challenges. Frontiers in Electronics, 4, 2023.
[3]
Keshari Nandan, Ateeb Naseer, and Yogesh S. Chauhan. Field-effect transistors based on two-dimensional materials (invited). Transactions of the Indian National Academy of Engineering, 8(1):1--14, Mar 2023.
[4]
Manish Chhowalla, Debdeep Jena, and Hua Zhang. Two-dimensional semiconductors for transistors. Nature Reviews Materials, 1(11):16052, Aug 2016.
[5]
Ling Tong, Jing Wan, Kai Xiao, Jian Liu, Jingyi Ma, Xiaojiao Guo, Lihui Zhou, Xinyu Chen, Yin Xia, Sheng Dai, Zihan Xu, Wenzhong Bao, and Peng Zhou. Heterogeneous complementary field-effect transistors based on silicon and molybdenum disulfide. Nature Electronics, 6(1):37--44, Jan 2023.
[6]
Wei Cao, Huiming Bu, Maud Vinet, Min Cao, Shinichi Takagi, Sungwoo Hwang, Tahir Ghani, and Kaustav Banerjee. The future transistors. Nature, 620(7974):501--515, Aug 2023.
[7]
Max C. Lemme, Deji Akinwande, Cedric Huyghebaert, and Christoph Stampfer. 2d materials for future heterogeneous electronics. Nature Communications, 13(1):1392, Mar 2022.
[8]
Supriyo Datta. Quantum Transport: Atom to Transistor. Cambridge University Press, 2005.
[9]
Tong Wu and Jing Guo. Speed up quantum transport device simulation on ferroelectric tunnel junction with machine learning methods. IEEE Transactions on Electron Devices, 67(11):5229--5235, 2020.
[10]
Tong Wu and Jing Guo. Multiobjective design of 2-d-material-based field-effect transistors with machine learning methods. IEEE Transactions on Electron Devices, 68(11):5476--5482, 2021.
[11]
Simon Thomann, Rodion Novkin, Jiajie Li, Yuting Hu, Jinjun Xiong, and Hussam Amrouch. Ml-tcad: Accelerating fefet reliability analysis using machine learning. IEEE Transactions on Electron Devices, 71(1):213--222, 2024.
[12]
Harsaroop Dhillon, Kashyap Mehta, Ming Xiao, Boyan Wang, Yuhao Zhang, and Hiu Yung Wong. Tcad-augmented machine learning with and without domain expertise. IEEE Transactions on Electron Devices, 68(11):5498--5503, 2021.
[13]
Preslav Aleksandrov, Ali Rezaei, Tapas Dutta, Nikolas Xeni, Asen Asenov, and Vihar Georgiev. Convolutional machine learning method for accelerating nonequilibrium green's function simulations in nanosheet transistor. IEEE Transactions on Electron Devices, 70(10):5448--5453, 2023.
[14]
Marius Bürkle, Umesha Perera, Florian Gimbert, Hisao Nakamura, Masaaki Kawata, and Yoshihiro Asai. Deep-learning approach to first-principles transport simulations. Phys. Rev. Lett., 126:177701, Apr 2021.
[15]
Paolo Giannozzi, Stefano Baroni, Nicola Bonini, Matteo Calandra, Roberto Car, Carlo Cavazzoni, Davide Ceresoli, Guido L Chiarotti, Matteo Cococcioni, Ismaila Dabo, Andrea Dal Corso, Stefano de Gironcoli, Stefano Fabris, Guido Fratesi, Ralph Gebauer, Uwe Gerstmann, Christos Gougoussis, Anton Kokalj, Michele Lazzeri, Layla Martin-Samos, Nicola Marzari, Francesco Mauri, Riccardo Mazzarello, Stefano Paolini, Alfredo Pasquarello, Lorenzo Paulatto, Carlo Sbraccia, Sandro Scandolo, Gabriele Sclauzero, Ari P Seitsonen, Alexander Smogunov, Paolo Umari, and Renata M Wentzcovitch. QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. Journal of Physics: Condensed Matter, 21(39):395502, sep 2009.
[16]
P Giannozzi, O Andreussi, T Brumme, O Bunau, M Buongiorno Nardelli, M Calandra, R Car, C Cavazzoni, D Ceresoli, M Cococcioni, N Colonna, I Carnimeo, A Dal Corso, S de Gironcoli, P Delugas, R A DiStasio, A Ferretti, A Floris, G Fratesi, G Fugallo, R Gebauer, U Gerstmann, F Giustino, T Gorni, J Jia, M Kawamura, H-Y Ko, A Kokalj, E Küçükbenli, M Lazzeri, M Marsili, N Marzari, F Mauri, N L Nguyen, H-V Nguyen, A Otero de-la Roza, L Paulatto, S Poncé, D Rocca, R Sabatini, B Santra, M Schlipf, A P Seitsonen, A Smogunov, I Timrov, T Thonhauser, P Umari, N Vast, X Wu, and S Baroni. Advanced capabilities for materials modelling with quantum ESPRESSO. Journal of Physics: Condensed Matter, 29(46):465901, oct 2017.
[17]
John P. Perdew, Kieron Burke, and Matthias Ernzerhof. Generalized gradient approximation made simple. Phys. Rev. Lett., 77:3865--3868, Oct 1996.
[18]
Ivo Souza, Nicola Marzari, and David Vanderbilt. Maximally localized wannier functions for entangled energy bands. Phys. Rev. B, 65:035109, Dec 2001.
[19]
Nicola Marzari, Arash A. Mostofi, Jonathan R. Yates, Ivo Souza, and David Vanderbilt. Maximally localized wannier functions: Theory and applications. Rev. Mod. Phys., 84:1419--1475, Oct 2012.
[20]
Samantha Bruzzone, Giuseppe Iannaccone, Nicola Marzari, and Gianluca Fiori. An open-source multiscale framework for the simulation of nanoscale devices. IEEE Transactions on Electron Devices, 61(1):48--53, 2014.
[21]
M. Büttiker, Y. Imry, R. Landauer, and S. Pinhas. Generalized many-channel conductance formula with application to small rings. Phys. Rev. B, 31:6207--6215, May 1985.
[22]
Ateeb Naseer, K. Nandan., Amit Agarwal, Somnath Bhowmick, and Yogesh Singh Chauhan. Performance evaluation of monolayer ZrS3 transistors for next-generation computing. IEEE Transactions on Electron Devices, 70(10):5435--5442, 2023.
[23]
Ateeb Naseer, Keshari Nandan, Amit Agarwal, Somnath Bhowmick, and Yogesh S. Chauhan. Di-metal chalcogenides: A new family of promising 2-D semiconductors for high-performance transistors. IEEE Transactions on Electron Devices, 70(5):2445--2452, 2023.
[24]
Ateeb Naseer, Somnath Bhowmick, Amit Agarwal, and Yogesh Singh Chauhan. Monolayer HfS3: A potential candidate for low-power and high-performance field-effect transistors. In 2024 8th IEEE Electron Devices Technology & Manufacturing Conference (EDTM), pages 1--3, 2024.
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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.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
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Published: 09 September 2024
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MLCAD '24
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MLCAD '24: 2024 ACM/IEEE International Symposium on Machine Learning for CAD
September 9 - 11, 2024
UT, Salt Lake City, USA
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MLCAD '24 Paper Acceptance Rate 35 of 83 submissions, 42%;
Overall Acceptance Rate 35 of 83 submissions, 42%
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