Wafer map analysis provides critical information for quality control and yield improvement tasks in semiconductor manufacturing. In particular, wafer patterns of gross failing areas (GFA) are important clues to identify the causes of relevant failures during the manufacturing process. In this work, a semi-supervised classification framework is proposed for wafer map analysis, and its application to wafer bin maps with GFA patterns classification is demonstrated. The Ladder network and the semi-supervised variational autoencoder are adopted to classify wafer bin maps in comparison with a standard convolutional neural network (CNN) model on two real-world datasets. The results have illustrated that two semi-supervised models are consistently and substantially better than the CNN model across various training data percentages by effective utilization of the unlabeled data. Active learning and pseudo labeling are also utilized to accelerate the learning curve.

中文翻译：

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一种用于晶圆图分类的半监督和增量建模框架

晶圆图分析为半导体制造中的质量控制和产量提高任务提供关键信息。特别是，总失效区域 (GFA) 的晶圆图案是识别制造过程中相关失效原因的重要线索。在这项工作中，提出了一种用于晶圆图分析的半监督分类框架，并展示了其在具有 GFA 图案分类的晶圆箱图上的应用。与两个真实世界数据集上的标准卷积神经网络 (CNN) 模型相比，采用梯形网络和半监督变分自编码器对晶圆箱图进行分类。结果表明，通过有效利用未标记数据，两个半监督模型在各种训练数据百分比上始终优于 CNN 模型。主动学习和伪标签也被用来加速学习曲线。