mlsanityData to 14 Oct 2026

TorchResist: Open-Source Differentiable Resist Simulator

arXiv 2502.06838 ↗First posted 6 Feb 2025cs.LGcited by 0

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Abstract (arXiv)

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 photoresist simulator.TorchResist employs 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 31 references that are in the map. First posted on arXiv 6 Feb 2025.

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References · 7

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2023GPT-4 Technical Reportcited by 18,396
  5. 2023DiffPattern: Layout Pattern Generation via Discrete Diffusioncited by 8
  6. 2024MoreauPruner: Robust Pruning of Large Language Models against Weight Perturbationscited by 5
  7. 2024ChatPattern: Layout Pattern Customization via Natural Languagecited by 4

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