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.

翻译：本文提出了一种结合机器学习与器件建模模拟的新型仿真方法。器件模拟基于量子力学非平衡格林函数（NEGF）方法，机器学习方法则是卷积生成网络的扩展。我们将这种新模拟方法命名为ML-NEGF，并在自研仿真器NESS（纳米电子学仿真软件）中实现了该方法。报告结果证明了ML-NEGF方法相比标准NEGF方法具有更快的收敛速度。经过训练的ML模型有效学习了纳米片晶体管行为的底层物理机制，从而加速了耦合泊松-NEGF模拟的收敛。定量分析显示，我们的ML-NEGF方法平均收敛加速率达60%，在保持相同精度的同时显著缩短了计算时间。