LithoBench PDE A benchmark dataset for PDE based computational lithography simulation, constructed by generating high fidelity 3D reference simulations for photomasks from the LithoBench dataset. Each sample contains intermediate 2D and 3D field data from the lithography simulation pipeline, providing ground truth input output pairs for three PDE learning tasks corresponding to three governing PDEs of photolithography. PDE Learning Tasks For each photomask, the reference simulation pipeline generates three ground truth input output pairs: Task PDE Mapping Input Output Shape Mask illumination Maxwell's equation M → E M (photomask) E (diffracted near field) [H, W] → [2, 2, H, W] (complex64) Post exposure bake Reaction diffusion equation h → m h (photoacid concentration) m (deprotection image) [25, H, W] → [25, H, W] Development Eikonal equation R → T R (development rate) T (development time) [25, H, W] → [25, H, W] M → E : Given a 2D photomask pattern, solve Maxwell's equations to predict the diffracted near field (DNF), represented as a 2×2 Jones matrix of the electric field (complex valued). Reference solutions are computed by rigorous coupled wave analysis (RCWA). h → m : Given a…
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