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The Cartographer
LithoDreamer: A Physics-Informed World Model for Multi-Stage Computational Lithography
June 25, 2026 Β· Grace Period Β· π Proceedings of the 43rd International Conference on Machine Learning (ICML), Jul. 6-11, 2026
Authors
Yuqi Jiang, Yumeng Liu, Zimu Li, Jinyuan Deng, Qian Jin, Yucheng Cui, Yu Li, Xunzhao Yin, Qi Sun, Cheng Zhuo
arXiv ID
2606.26713
Category
cs.AI: Artificial Intelligence
Citations
0
Venue
Proceedings of the 43rd International Conference on Machine Learning (ICML), Jul. 6-11, 2026
Abstract
As semiconductor technology nodes scale, computational lithography is essential for ensuring yield and performance. However, lithography is a continuous physical process involving mask optimization, optical imaging, resist exposure, and development, which existing models fail to capture. To overcome this limitation, we present LithoDreamer, the first physics-informed World Model (WM) framework for computational lithography, which formulates the ``Layout-Mask-Resist Image-After Development Image (ADI)'' pipeline as a decision-driven multi-step evolution system. LithoDreamer captures feature changes between adjacent states to model stage-specific physics-informed latent spaces, in which it controls process intervention exploration and drives subsequent state transitions. To achieve interpretable intervention optimization without continuous supervision, we propose a contrastive variational optimization paradigm that contrasts the latent differences between intervention paths with variational evolution constraints, guiding the model to generate evolutions consistent with real lithography physics. Experiments show LithoDreamer achieves state-of-the-art performance in forward evolution and inverse planning. Our lithography dataset is publicly available at GitHub (https://github.com/7jiangyq/lithodreamer.git).
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