基于迁移学习与深度森林的晶圆图缺陷识别

为了有效识别晶圆图缺陷模式并及时诊断制造过程的故障源，提出基于迁移学习和深度森林集成的DenseNet-GCForest晶圆图缺陷模式识别模型. 为了解决深度学习模型训练困难和晶圆图缺陷类型数目不平衡的问题，利用迁移学习将深度卷积神经网络DenseNet在ImageNet上预训练的网络权重参数迁移至本模型并重新设计分类层，以减少深度网络模型的训练时间并提高模型的特征提取能力；基于DenseNet网络提取的高维抽象晶圆图特征，引入深度森林模型进行晶圆图特征缺陷模式识别. 工业案例的实验验证结果表明，该方法的识别准确率达到了96.8%，并提高了识别效率，其性能优于典型的卷积神经网络以及其他常用识别方法.

Wafer map defect recognition based on transfer learning and deep forest

A wafer map pattern recognition (WMPR) model was proposed based on transfer learning and deep forest, in order to identify the defect pattern of the wafer maps and to timely diagnose the source of the fault in the manufacturing process. Transfer learning was used to migrate the network weight parameters of the deep CNN DenseNet pre-trained on ImageNet to this model, and the classification layer of the model was redesigned, in order to solve the problems of difficulties of deep learning model training and imbalance in the number of defect types in wafer maps. Thus, the training time of the model was reduced and the feature extraction ability was improved. Deep forest model was introduced to identify the wafer defect pattern, based on the abstract features of the wafer maps extracted by DenseNet. The experimental results on an industrial case demonstrated that the average recognition rate was about 96.8%. This method can improve the recognition efficiency and its performance is better than those well-known CNNs and other typical classifiers.