用视觉Transformer实现半导体缺陷自动分类，少样本下准确率超90%。
Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers
- 基于ViT的监督与半监督学习框架，适配纳米级SEM图像
- 每类缺陷仅需15张以下图像，准确率超过90%
- 支持迁移学习，适合工厂快速部署和灵活扩展
半导体工艺中的缺陷控制对提升良率、降低成本及防止器件失效至关重要。电子束成像常用于晶圆在线检测，但人工分类受限于时间、人力和主观偏差。近年来，深度学习在工业图像检测中表现出色。本文提出使用视觉变换器（ViT）对IBM Albany工厂300mm晶圆的扫描电子显微镜（SEM）图像进行自动缺陷分类（ADC）。我们分析了超过7400张图像中的11种缺陷类型，研究了DinoV2迁移学习与半监督学习在提升分类精度和计算效率方面的潜力。实验表明，每类缺陷仅需少于15张图像即可实现90%以上的分类准确率。本工作展示了该框架作为平台无关的内建分类工具，在缩短交付周期和增强灵活性方面的应用前景。
原文摘要 · Abstract (English)
Controlling defects in semiconductor processes is important for maintaining yield, improving production cost, and preventing time-dependent critical component failures. Electron beam-based imaging has been used as a tool to survey wafers in the line and inspect for defects. However, manual classification of images for these nano-scale defects is limited by time, labor constraints, and human biases. In recent years, deep learning computer vision algorithms have shown to be effective solutions for image-based inspection applications in industry. This work proposes application of vision transformer (ViT) neural networks for automatic defect classification (ADC) of scanning electron microscope (SEM) images of wafer defects. We evaluated our proposed methods on 300mm wafer semiconductor defect data from our fab in IBM Albany. We studied 11 defect types from over 7400 total images and investigated the potential of transfer learning of DinoV2 and semi-supervised learning for improved classification accuracy and efficient computation. We were able to achieve classification accuracies of over 90% with less than 15 images per defect class. Our work demonstrates the potential to apply the proposed framework for a platform agnostic in-house classification tool with faster turnaround time and flexibility.
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