A wafer bin map (WBM), which is the result of an electrical die-sorting test, provides information on which bins failed what tests, and plays an important role in finding defective wafer patterns in semiconductor manufacturing. Current wafer inspection based on WBM has two problems: good/bad WBM classification is performed by engineers and the bin code coloring scheme does not reflect the relationship between bin codes. To solve these problems, we propose a neural network-based bin coloring method called Bin2Vec to make similar bin codes are represented by similar colors. We also build a convolutional neural network-based WBM classification model to reduce the variations in the decisions made by engineers with different expertise by learning the company-wide historical WBM classification results. Based on a real dataset with a total of 27,701 WBMs, our WBM classification model significantly outperformed benchmarked machine learning models. In addition, the visualization results of the proposed Bin2Vec method makes it easier to discover meaningful WBM patterns compared with the random RGB coloring scheme. We expect the proposed framework to improve both efficiencies by automating the bad wafer classification process and effectiveness by assigning similar bin codes and their corresponding colors on the WBM.
The Electrical die sorting (EDS) test is performed to discriminate defective wafers for the purpose of improving the yield of the wafers during the semiconductor manufacturing process, and wafer maps are generated as a result. Semiconductor manufacturing process and equipment engineers use the patterns of the wafer map based on their knowledge to judge the defective wafer and estimate the cause. We use convolutional neural network which demonstrate good performance in the image classification. The convolutional neural network is used as a classification model of which the image of wafer map itself as input and whether the image is good or bad as output. While previous studies have used hand-crafted features for wafer map-based fault detection, the methodology used in this study is that the convolutional neural network learns the features useful for classification, it has the advantage of integrating knowledge. We show that the proposed classifier has better prediction accuracy than the conventional machine learning based techniques such as multilayer perceptron and random forest empirically by experiments on the data collected in the actual semiconductor manufacturing process.
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