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- W4386875571 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." @default.
- W4386875571 created "2023-09-20" @default.
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- W4386875571 date "2023-09-17" @default.
- W4386875571 modified "2023-09-27" @default.
- W4386875571 title "Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Greens Function Simulations" @default.
- W4386875571 doi "https://doi.org/10.48550/arxiv.2309.09374" @default.
- W4386875571 hasPublicationYear "2023" @default.
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