LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing Supplementary Materials

It also incorporates Python programs that can train and test the models mentioned in this paper. By inheriting the classes, users can easily build their own models that can be trained and tested by LithoBench, without the need of writing the code for data loading and evaluation. For average pooling, we use a kernel size of 7 and a stride of 1. PyTorch builtin functions so that an SGD optimizer with a learning rate of 0.5 can be used to optimize Table 1 compares the performance of our reference IL T algorithm with SOT A IL T algorithms. We provide the PNG images of the all data. The connections between adjacent vertices are horizontal or vertical. In this section, we describe the details of the DNN models used in this paper.