Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Greens Function Simulations

• URL: http://arxiv.org/abs/2309.09374v1
• Date: Sun, 17 Sep 2023 20:43:54 GMT
• Title: Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Greens Function Simulations
• Authors: Preslav Aleksandrov, Ali Rezaei, Nikolas Xeni, Tapas Dutta, Asen Asenov, Vihar Georgiev
• Abstract summary: This work describes a novel simulation approach that combines machine learning and device modelling simulations. We have named our new simulation approach ML-NEGF and we have implemented it in our in-house simulator called NESS.
• Score: 0.0879626117219674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work describes a novel simulation approach that combines machine learning and device modelling simulations. The device simulations are based on the quantum mechanical non-equilibrium Greens 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.

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