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

Umar Rashid^1,2, Fahad Shafique¹, Hamza Atif¹

¹Department of Electrical Engineering, University of Engineering and Technology, New Campus, Lahore, Pakistan.

Scientific Reports

|March 16, 2026

Summary

Adaptive Reinforcement Learning for Lithography Optimization (ARLO) enhances semiconductor lithography by reducing pattern errors. This AI approach optimizes photomasks for sub-nanometer integrated circuit fabrication, improving precision and efficiency.

Keywords:

Computational lithography Deep neural networks Inverse lithography technology Mask optimization Reinforcement learning Semiconductor manufacturing

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Area of Science:

Semiconductor Manufacturing
Computational Lithography
Artificial Intelligence in Engineering

Background:

Semiconductor lithography is critical for integrated circuit (IC) fabrication, representing ~30% of costs.
Shrinking feature sizes to sub-nanometer scales introduce optical diffraction and distortion challenges.
Traditional Optical Proximity Correction (OPC) is insufficient; Inverse Lithography Technology (ILT) is computationally intensive.

Purpose of the Study:

To introduce Adaptive Reinforcement Learning for Lithography Optimization (ARLO) for advanced photomask optimization.
To address the scalability and computational complexity limitations of existing ILT methods.
To improve pattern fidelity and reduce process variations in semiconductor lithography.

Main Methods:

Developed a U-Net-based framework integrating self-attention and reinforcement learning (RL).
Employed iterative photomask optimization using real-time lithographic simulations.
Evaluated performance on the LithoBench benchmark against state-of-the-art methods.

Main Results:

ARLO achieved a 37.8% reduction in [Formula: see text] Loss and 74.0% reduction in Process Variation Band (PVB) versus GAN-OPC.
Demonstrated significant improvements over Deep LithoNet (DLN) and RL-ILT in both [Formula: see text] Loss and PVB.
Maintained a competitive runtime of 0.035 seconds per patch despite increased shot counts.

Conclusions:

ARLO presents a scalable and efficient solution for next-generation semiconductor manufacturing.
The proposed framework effectively optimizes photomasks, enhancing pattern fidelity and reducing process variations.
ARLO offers a promising AI-driven approach to overcome challenges in sub-nanometer lithography.