Flow-Litho: A Physics-Guided World Model for Faithful Process Responses in Multi-Stage Computational Lithography

Computational lithography improves pattern fidelity and manufacturability in advanced integrated circuit (IC) fabrication through modeling and optimization of pattern transfer. However, process responses vary substantially across stages and local regions, making reliable multi-stage lithography modeling challenging. To this end, we propose FlowLitho, a physics-guided lithography world model (WM) for faithful process-response modeling, which recursively evolves the "Layout-Mask-Resist Image-ADI" pipeline through intra-stage physics correction and cross-stage difference preservation. First, we formulate the heterogeneous multi-stage process as a latent-space flow-matching problem, learning process-conditioned velocity fields and evolving latent states through numerical integration. Second, we develop a physical dynamics correction mechanism that evaluates regional response discrepancies using stage-specific approximate physical operators and couples their gradients to correct the latent velocity field. Finally, we introduce a difference-preserving self-evolving optimization paradigm for joint training, which uses paired supervision for the same layout across process conditions to guide intermediate-state recomposition and downstream replay, enabling the model to preserve process-induced differences while adapting to upstream rollout deviations. Experiments show that Flow-Litho achieves state-of-the-art performance in multi-stage lithography modeling and process-parameter planning.