mlsanityData to 7 Oct 2026

LithoDreamer: A Physics-Informed World Model for Multi-Stage Computational Lithography

arXiv 2606.26713 ↗First posted 25 Jun 2026cs.AIcited by 0

On the map: World models → Latent predictive world models

Abstract (arXiv)

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).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Latent predictive world models, one of 11 directions in World models. Builds on 11 of its 21 references that are in the map. First posted on arXiv 25 Jun 2026.

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11 of 21 references.

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  1. 2024DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning7 Nov

Other references · 10

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2022Scalable Diffusion Models with Transformerscited by 5,931
  3. 2024Diffusion for World Modeling: Visual Details Matter in Ataricited by 285
  4. 2024Navigation World Modelscited by 262
  5. 2024DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generationcited by 186
  6. 2016Variational Autoencoder for Deep Learning of Images, Labels and Captionscited by 137
  7. 2025LaDi-WM: A Latent Diffusion-based World Model for Predictive Manipulationcited by 24
  8. 2024FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queriescited by 12
  9. 2020From IC Layout to Die Photo: A CNN-Based Data-Driven Approachcited by 6
  10. 2024Intelligent OPC Engineer Assistant for Semiconductor Manufacturingcited by 2

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  4. 2026Failure-Aware Refinement of Vision-Language Model for Lithography Defect Detection
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