Abstract Semiconductor lithography, a pivotal process in integrated circuit (IC) fabrication, accounts for approximately 30% of production costs and faces significant challenges as feature sizes shrink to sub-nanometer scales. Optical diffraction and process-induced distortions complicate precise patterning, necessitating advanced techniques beyond traditional Optical Proximity Correction (OPC). Inverse Lithography Technology (ILT) offers a mathematically robust approach to enhance pattern fidelity, yet its high computational complexity limits scalability. We propose Adaptive Reinforcement Learning for Lithography Optimization (ARLO), a U-Net-based framework integrating self-attention mechanisms and reinforcement learning (RL) to iteratively optimize photomasks using real-time lithographic simulations. Evaluated on the LithoBench benchmark, ARLO achieves a 37. 8% reduction in L₂ Loss and a 74. 0% reduction in Process Variation Band (PVB) compared to GAN-OPC, alongside 14. 7% and 9. 1% L₂ Loss reductions and 51. 3% and 37. 1% PVB reductions versus Deep LithoNet (DLN) and RL-ILT, respectively. Despite a higher shot count (181. 4% increase vs. GAN-OPC, 59. 0% vs. DLN-1, 29. 4% vs. RL-ILT), ARLO maintains a competitive runtime of 0. 035 seconds per patch. These results position ARLO as a scalable, efficient solution for next-generation semiconductor manufacturing.
Rashid et al. (2026) studied this question.