Aleksandrov, P. , Rezaei, A. , Dutta, T. , Xeni, N., Asenov, A. and Georgiev, V. (2023) Convolutional machine learning method for accelerating non-equilibrium Green’s function simulations in nanosheet transistor. IEEE Transactions on Electron Devices, 70(10), pp. 5448-5453. (doi: 10.1109/TED.2023.3306319)
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Abstract
This work describes a novel simulation approach that combines machine learning (ML) and device modeling simulations. The device simulations are based on the quantum mechanical nonequilibrium Green’s function (NEGF) approach, and the ML method is an extension of a convolutional generative network. We have named our new simulation approach ML-NEGF. It is implemented in our in-house simulator called Nano-Electronics Simulation Software (NESS). The reported results demonstrate the improved convergence speed of the ML-NEGF method in comparison to the “standard” NEGF approach. The trained ML model effectively learns the underlying physics of nano-sheet transistor behavior, resulting in faster convergence of the coupled Poisson-NEGF self-consistency simulations. Quantitatively, our ML-NEGF approach achieves an average convergence speedup of 60%, substantially reducing the computational time while maintaining the same accuracy.
| Item Type: | Articles |
|---|---|
| Additional Information: | This research was funded by the Engineering and Physical Sciences Research Council (EPSRC), through Grant No. EP/S001131/1 and EP/P009972/1. This project has also received funding from the EPSRC Impact Acceleration Account scheme under Grant Agreement No. EP/R511705/1. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Asenov, Professor Asen and Dutta, Dr Tapas and Rezaei, Dr Ali and Georgiev, Professor Vihar and Xeni, Mr Nikolas and Aleksandrov, Mr Preslav |
| Authors: | Aleksandrov, P., Rezaei, A., Dutta, T., Xeni, N., Asenov, A., and Georgiev, V. |
| College/School: | College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering |
| Journal Name: | IEEE Transactions on Electron Devices |
| Publisher: | IEEE |
| ISSN: | 0018-9383 |
| ISSN (Online): | 1557-9646 |
| Published Online: | 28 August 2023 |
| Copyright Holders: | Copyright © 2023 IEEE |
| First Published: | First published in IEEE Transactions on Electron Devices 70(10):5448 - 5453 |
| Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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