A Wafer Map Defect Pattern Classification Model Based on Deep Convolutional Neural Network
A Wafer Map Defect Pattern Classification Model Based on Deep Convolutional Neural Network
复制标题
基于深度卷积神经网络的晶圆图缺陷模式分类模型
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
Zheng Shi
中科院分区：
文献类型：
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作者：
Dong;Zheng Shi
集成电路（IC）制造中的许多过程问题都会导致晶圆图上的某些特定缺陷模式形成。可以通过对晶圆映射缺陷模式（WMDP）进行分类来找到该过程问题。本文提出了一种易于训练的深度卷积神经网络（DCNN）分类模型，该模型通过使用全球平均池和参数减少方法，其WMDP的识别率很高。该模型在基准数据集上达到了94.68％的平均识别率，该识别率比基于人工设计的功能和具有很多参数的神经网络的模型要好得多
Many process problems in the Integrated Circuit (IC) manufacturing can lead to the formation of some specific defect patterns on the wafer map. The process problems can be located by classifying wafer map defect patterns (WMDPs). This paper proposed an easy-to-train deep convolutional neural network (DCNN) classification model with a high recognition rate for WMDP by using the global average pooling and parameter reducing method. This model achieved a 94.68% average recognition rate on a benchmark dataset, which is much better than the model based on artificially-designed-features and neural networks with lots of parameters