Appl. Sci. 2019, 9(3), 597; https://doi.org/10.3390/app9030597
Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification
School of Industrial Management Engineering, Korea University, Seoul 02841, Korea
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Received: 3 January 2019 / Revised: 30 January 2019 / Accepted: 6 February 2019 / Published: 11 February 2019
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
Abstract
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. View Full-TextKeywords:
wafer bin map (WBM); Bin2Vec; Word2Vec; bad wafer classification; convolution neural network
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Kim, J.; Kim, H.; Park, J.; Mo, K.; Kang, P. Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification. Appl. Sci. 2019, 9, 597.
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