A Light-Weight Neural Network for Wafer Map Classification Based on Data Augmentation

Tsung Han Tsai, Yu Chen Lee

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


In the semiconductor industry, the testing section has always played an important role. The testing section often requires engineers to judge the defect, which wastes a lot of time and cost. The accurate classification can provide useful information for engineers through neural networks. In this paper, we present a method for wafer map data augmentation and defect classification. Data augmentation is based on CNN encoder-decoder and the classification is based on depthwise separable convolutions. There are two datasets used, one is open dataset WM-811K and the other is built with a Taiwan company. We train two models with mobilenetV1 and V2 for the different datasets. The light-weight deep convolution can reduce model parameters and calculations, which is very efficient for the testing house with large production volumes. On two different data sets, our proposed method can reduce the number of parameters by 30% and 95%, and reduce the amount of calculation by 75% and 25%, respectively. The test accuracy of the first dataset is 93.95%. The second dataset test accuracy is 87.04%. After the data augmentation, accuracy is increased to 97.01% and 95.09%, respectively.

Original languageEnglish
Article number9153016
Pages (from-to)663-672
Number of pages10
JournalIEEE Transactions on Semiconductor Manufacturing
Issue number4
StatePublished - Nov 2020


  • Deep learning
  • data augmentation
  • depthwise separable convolution
  • wafer defects


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