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<p>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 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(L_2\)</EquationSource> </InlineEquation> Loss and a 74.0% reduction in Process Variation Band (PVB) compared to GAN-OPC, alongside 14.7% and 9.1% <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(L_2\)</EquationSource> </InlineEquation> 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.</p>

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Adaptive reinforcement learning for lithography optimization: a scalable AI-driven solution for next-generation semiconductor manufacturing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月15日

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  • Umar Rashid,
  • Fahad Shafique,
  • Hamza Atif,
  • Waleed Waheed,
  • Rizwan Khan,
  • Muhammad Akmal

摘要

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 \(L_2\) Loss and a 74.0% reduction in Process Variation Band (PVB) compared to GAN-OPC, alongside 14.7% and 9.1% \(L_2\) 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.