TORCWA: GPU-accelerated Fourier modal method and gradient-based optimization for metasurface design

TORCWA is an electromagnetic wave simulation and optimization tool utilizing rigorous coupled-wave analysis. One of the advantages of TORCWA is that it provides GPU-accelerated simulation. It shows a greatly accelerated simulation speed compared to when the same simulation is performed on a CPU-based. Although it has accelerated speed, the simulation results are almost identical to the commercialized electromagnetic wave simulations. The second advantage is that it provides GPU-accelerated gradient calculation for the simulation results with reverse-mode automatic differentiation of PyTorch version 1.10.1. In particular, the instability of gradient calculation of eigendecomposition is also improved. With this property, TORCWA can be utilized for the optimization of various nanophotonic devices. Here, we first introduce the formulation used in TORCWA, compare it with other commercial simulations, and show the computational performance in multiple environments. Then, the gradient calculation and optimization examples are shown. Thanks to accelerated computational performance and gradient calculation, TORCWA is a worthy program for designing and optimizing various nanophotonic devices.

TORCWA是一款基于严谨耦合波分析的电磁波仿真与优化工具。该工具的一大优势在于其提供的GPU加速仿真功能，相较于基于CPU的仿真，它能显著提升仿真速度。尽管加速后的仿真速度有所提高，但仿真结果与商业化电磁波仿真几乎一致。其另一大优势在于，它支持使用PyTorch版本1.10.1的逆模式自动微分进行GPU加速的梯度计算，尤其是对特征值分解梯度计算的稳定性也有所改善。凭借这一特性，TORCWA可应用于各类纳米光子器件的优化。在此，我们首先介绍了TORCWA中的公式，并将其与其他商业仿真进行了比较，展示了在多个环境中的计算性能。随后，展示了梯度计算和优化实例。得益于加速的计算性能和梯度计算，TORCWA是一款设计优化各类纳米光子器件的优选程序。