LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing

Computational lithography provides algorithmic and mathematical support for resolution enhancement in optical lithography, which is critical for semiconductor manufacturing. The time-consuming lithography simulation and mask optimization processes limit the practical application of inverse lithography technology (ILT), a promising solution to the challenges of advanced-node lithography. Although machine learning for ILT has shown promise for reducing the computational burden, this field lacks a dataset that can train the models thoroughly and evaluate the performance comprehensively. To boost the development of AI-driven computational lithography, we present the LithoBench dataset, a collection of circuit layout tiles for deep-learning-based lithography simulation and mask optimization. LithoBench consists of more than 120k tiles that are cropped from real circuit designs or synthesized according to topologies of widely adopted ILT testcases. Ground truths are generated by a famous lithography model in academia and an advanced ILT method. We provide a framework to design and evaluate deep neural networks (DNNs) with the data. The framework is used to benchmark state-of-the-art models on lithography simulation and mask optimization. LithoBench is available at https://github.com/shelljane/lithobench.