AI-Driven Defect Dataset Generation for Optical Lithography
Published:Dec 9, 2025 06:13
•1 min read
•ArXiv
Analysis
This research explores an innovative approach to creating datasets for defect detection in optical lithography, a critical step in semiconductor manufacturing. The study's focus on a physics-constrained and design-driven methodology suggests a potentially more accurate and efficient approach to training AI models for defect identification.
Key Takeaways
- •The methodology leverages a physics-constrained approach, indicating a grounding in real-world physical principles.
- •It's design-driven, highlighting an emphasis on practical application and manufacturing processes.
- •The research's objective is to improve defect dataset generation for better AI model training in lithography.
Reference
“The research focuses on generating defect datasets for optical lithography.”