Manhattan Assembly: Differentiable Mask Optimization with Manhattan Primitives

Inverse Lithography Technology (ILT) formulates mask optimization as an inverse-design problem subject to manufacturing constraints, yet existing methods struggle to jointly balance printing fidelity, manufacturability, and scale. We present $\text{Manhattan Assembly}$, a primitive-based framework that represents photomasks as axis-aligned rectangular primitives and optimizes their continuous geometry through a differentiable renderer while preserving Manhattan structure by construction. A lithography-guided adaptive refinement strategy adjusts primitive allocation through splitting and pruning, concentrating representation capacity in critical hotspot regions, while global primitive sharing with window-wise computation enables full-chip optimization. Experiments on ICCAD13-S and full-chip layouts from real chip designs demonstrate improved printing fidelity and process-window robustness with reduced mask complexity. In the full-chip setting, Manhattan Assembly reduces $L_2$ and PVB by 34.2% and 9.1%, respectively, relative to the best SOTA baseline for each metric.