Recent decades have witnessed remarkable advancements in artificial intelligence (AI), including large language models (LLMs), image and video generative models, and embodied AI systems. These advancements have led to an explosive increase in the demand for computational power, challenging the limits of Moore's Law. Optical lithography, a critical technology in semiconductor manufacturing, faces significant challenges due to its high costs. To address this, various lithography simulators have been developed. However, many of these simulators are limited by their inadequate photoresist modeling capabilities. This paper presents TorchResist, an open-source, differentiable photoresistthis http URLemploys an analytical approach to model the photoresist process, functioning as a white-box system with at most twenty interpretable parameters. Leveraging modern differentiable programming techniques and parallel computing on GPUs, TorchResist enables seamless co-optimization with other tools across multiple related tasks. Our experimental results demonstrate that TorchResist achieves superior accuracy and efficiency compared to existing solutions. The source code is publicly available.
View on arXiv@article{wang2025_2502.06838,
title={ TorchResist: Open-Source Differentiable Resist Simulator },
author={ Zixiao Wang and Jieya Zhou and Su Zheng and Shuo Yin and Kaichao Liang and Shoubo Hu and Xiao Chen and Bei Yu },
journal={arXiv preprint arXiv:2502.06838},
year={ 2025 }
}