Unsupervised Pre-Training of Imbalanced Data for Identification of Wafer Map Defect Patterns

Abstract

Visual defect inspection and classification are significant steps of most manufacturing processes in the semiconductor and electronics industries. Known and unknown defects on wafer maps tend to cluster, and these spatial patterns provide valuable process information for supporting manufacturing in determining the root causes of abnormal processes. In previous studies, data augmentation-based deep learning (DL) techniques were most commonly used for the identification of wafer map defect patterns (WMDP). Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations were manually designed for the WMDP problem. In this study, we propose a DL-based method with automatic data augmentation for the WMDP task. Basically, it focuses on learning effective discriminative features, from wafer maps, through a deep network structure. The network consists of a convolution-based variational autoencoder (CVAE) sequentially. First, we pre-trained the CVAE on large training data in an unsupervised manner. Second, we fine-tuned the encoder of the CVAE, which was followed by a neural network (NN) classifier, in a supervised manner. Additionally, we describe a simple procedure for automatically searching for improved data augmentation policies. The policy mainly consists of five image processing functions: rotation, flipping, shifting, shearing range, and zooming. The effectiveness of the proposed method was demonstrated through experimental results obtained from a simulation dataset and a real-world wafer map dataset (WM-811K). This study provides guidance for the application of deep learning in semiconductor manufacturing processes to improve product quality and yield.INDEX TERMS Classification, convolutional variational autoencoder, deep learning, imbalanced data, neural network, unsupervised pre-training, variational autoencoder, wafer map defect patterns.

A sight on defect detection methods for imbalanced industrial data

“…Jain et al [9] suggested a data augmentation method using Generative adversarial networks to generate synthetic data then they used Convolutional Neural Network to classify surface defects in hot-rolled steel strips . Shon et al [10] proposed automatic data augmentation with : rotation, flipping, shifting, shearing range, and zooming techniques and deep learning method to identify defects of wafer. Zheng et al [11] developed a generic semi-supervised deep learning model for automated surface inspection using data augmentation.…”

Development of a Wafer Defect Pattern Classifier Using Polar Coordinate System Transformed Inputs and Convolutional Neural Networks

“…Considering this, research has been conducted using unsupervised learning methods, which are useful for handling large amounts of unlabeled data. Shon et al presented a methodology to train a convolution-based variational autoencoder (CVAE) in an unsupervised manner [21], and Qiao Xu et al used unlabeled data to learn common defect patterns using an unsupervised method [22]. However, only a few studies have used pure unsupervised learning.…”

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

“…Regarding our selected set of papers, the most frequently used and probably the most successful data mining method for defect product detection or classification is the Neural network. Most recently, for example, the article "Unsupervised Pre-Training of Imbalanced Data for Identification of Wafer Map Defect Patterns" written by Shon et al (2021), where the proposed model is based on convolution variational autoencoder (CVAE), achieved a 95.1% level of F-measure. In our analysis, we found out that more than 42% of composed primary studies implementing NN are focused on CNN, for example, Lin et al (2019), Wang et al (2018) 2020), Cerezci et al (2020) or Jiang et al (2021).…”