AD-NEGF: An End-to-End Differentiable Quantum Transport Simulator for Sensitivity Analysis and Inverse Problems

Zhanghao Zhouyin, Xiang Chen, Peng Zhang, Jun Wang, Lei Wang, Hong Guo

Keywords: Quantum Transport, Non-Equilibrium Green Function, Automatic Differentiation, Differentiable Programming, Deep Learning, Sensitivity Analysis, Inverse Design

TL;DR: We provide to the best of our knowledge the first end-to-end differentiable quantum transport simulator, which can compute differential quantities and perform atomic level device optimization.

Abstract: Quantum transport theory describes transport phenomena from first principles, which is essential for domains such as semiconductor fabrication. As a representative, the Non-Equilibrium Green Function (NEGF) method achieves superiority in numerical accuracy. However, its tremendous computational cost makes it unbearable for high-throughput simulation tasks such as sensitivity analysis, inverse design, etc. In this work, we propose AD-NEGF, to the best of our knowledge the first Automatic Differentiation (AD) based quantum transport simulator. AD-NEGF calculates gradient information efficiently by utilizing automatic differentiation and implicit layer techniques, while guaranteeing the correctness of the forward simulation. Such gradient information enables accurate and efficient calculation of differential physical quantities and solving inverse problems that are intractable by traditional optimization methods.

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