用深度学习从CAD生成仿真晶圆图，解决InP晶圆缺标准品的缺陷检测难题。
Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection
- 用深度神经网络从CAD数据生成逼真晶圆图像，替代缺失的良品标准
- 在真实晶圆照片上验证，仿真图像质量优于传统决策树方法
- 可灵活应用在任意区域，提升小批量晶圆缺陷检测效率
半导体制造中的质量管理通常依赖已知良品标准的模板匹配。针对铟磷（InP）多项目晶圆制造，因产量低、设计变化大，良品标准常无法获取，导致缺陷检测依赖人工且耗时。本文提出一种基于深度学习的方法，利用神经网络从CAD数据生成逼真晶圆图像，构建合成良品标准。评估了多种训练目标，并在合成数据和真实InP晶圆图像上验证生成效果。结果表明，该方法优于基线决策树方法，可基于CAD设计在任意区域生成“仿真良品单元”，用于更高效的表面缺陷检测。我们将其应用于模板匹配流程，展示了其在实际场景中的可行性与价值。
原文摘要 · Abstract (English)
Quality management in semiconductor manufacturing often relies on template matching with known golden standards. For Indium-Phosphide (InP) multi-project wafer manufacturing, low production scale and high design variability lead to such golden standards being typically unavailable. Defect detection, in turn, is manual and labor-intensive. This work addresses this challenge by proposing a methodology to generate a synthetic golden standard using Deep Neural Networks, trained to simulate photo-realistic InP wafer images from CAD data. We evaluate various training objectives and assess the quality of the simulated images on both synthetic data and InP wafer photographs. Our deep-learning-based method outperforms a baseline decision-tree-based approach, enabling the use of a 'simulated golden die' from CAD plans in any user-defined region of a wafer for more efficient defect detection. We apply our method to a template matching procedure, to demonstrate its practical utility in surface defect detection.
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