Aleksandrov, P. , Rezaei, A. , Xeni, N., Dutta, T. , Asenov, A. and Georgiev, V. (2023) Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Green’s Function Simulations. In: 2023 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD), Kobe, Japan, 27-29 September 2023, pp. 169-172. ISBN 9784863488038 (doi: 10.23919/SISPAD57422.2023.10319587)
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Abstract
This work describes a novel simulation approach that combines machine learning and device modeling simulations. The device simulations are based on the quantum mechanical non-equilibrium Green’s function (NEGF) approach and the machine learning method is an extension to a convolutional generative network. We have named our new simulation approach ML-NEGF and we have implemented it in our in-house simulator called NESS (nano-electronics simulations software). 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 behaviour, resulting in faster convergence of the coupled Poisson-NEGF simulations. Quantitatively, our ML-NEGF approach achieves an average convergence acceleration of 60%, substantially reducing the computational time while maintaining the same accuracy.
| Item Type: | Conference Proceedings |
|---|---|
| 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 (Nano-Electronic Simulation Software (NESS)—creating the first open source TCAD platform in the world and Fast Track - Development boost for the Device Modelling group opensource NESS computational framework). |
| 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., Xeni, N., Dutta, T., Asenov, A., and Georgiev, V. |
| College/School: | College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering |
| ISBN: | 9784863488038 |
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