基于深度学习的多项目InP晶圆仿真用于无监督表面缺陷检测
Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection
AI总结：
针对InP多项目晶圆缺金标准、缺陷检测依赖人工的问题，提出用深度神经网络从CAD生成仿真金标准晶圆图像的方法，性能优于决策树基线，可提升缺陷检测效率。
摘要：
半导体制造中的质量管理通常依赖于与已知金标准的模板匹配。对于磷化铟（Indium-Phosphide，InP）多项目晶圆制造而言，生产规模小且设计可变性高，导致这类金标准通常无法获取，相应的缺陷检测工作依赖人工，劳动强度大。本研究针对这一挑战，提出了一种利用深度神经网络生成合成金标准的方法，该网络经训练可从CAD数据仿真生成照片级真实感的InP晶圆图像。我们评估了多种训练目标，并在合成数据和InP晶圆照片上评估了仿真图像的质量。我们的基于深度学习的方法优于基于决策树的基线方法，能够从CAD版图中生成“仿真金标准裸片”，可应用于晶圆的任意用户定义区域，以实现更高效的缺陷检测。我们将该方法应用于模板匹配流程，以验证其在表面缺陷检测中的实际效用。
英文摘要：
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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