A Physics-Constrained, Design-Driven Methodology for Defect Dataset Generation in Optical Lithography

📝 Original Info

• Title: A Physics-Constrained, Design-Driven Methodology for Defect Dataset Generation in Optical Lithography
• ArXiv ID: 2512.09001
• Date: 2025-12-09
• Authors: Yuehua Hu, Jiyeong Kong, Dong-yeol Shin, Jaekyun Kim, Kyung-Tae Kang

📝 Abstract

The efficacy of Artificial Intelligence (AI) in micro/nano manufacturing is fundamentally constrained by the scarcity of high-quality and physically grounded training data for defect inspection. Lithography defect data from semiconductor industry are rarely accessible for research use, resulting in a severe shortage of publicly available datasets. To address this bottleneck in lithography, this study proposes a novel methodology for generating large-scale, physically valid defect datasets with pixel-level annotations. The framework begins with the ab initio synthesis of defect layouts using controllable, physics-constrained mathematical morphology operations (erosion and dilation) applied to the original design-level layout. These synthesized layouts, together with their defect-free counterparts, are fabricated into physical samples via high-fidelity digital micromirror device (DMD)-based maskless lithography. Optical microscope images of the synthesized defect samples and their defect-free references are then compared to create consistent defect delineation annotations. Using this methodology, we constructed a comprehensive dataset of 3,530 Optical microscope images containing 13,36...

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