LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing Supplementary Materials

In addition to the data and data loaders, LithoBench also provides functionalities that can facilitate the development of DNN-based and traditional ILT algorithms. Based on PyTorch [1] and OpenILT [2], we implement the reference lithography simulation model as a PyTorch module, which can be used like a DNN layer. The GPU-based fast Fourier transform (FFT) can boost the speed of lithography simulation. PyTorch optimizers can be directly employed to optimize the masks according to ILT loss functions, significantly simplifying the development of ILT algorithms. To evaluate ILT results, LithoBench provides a simple interface to measure the L2 loss, PVB, EPE, and shots of the output masks.