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Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification
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 cla...
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Published in: | Applied sciences 2019-02, Vol.9 (3), p.597 |
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description | 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. |
doi_str_mv | 10.3390/app9030597 |
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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. 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Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c361t-bc1b79b377db2c02c13e8f9d8acea3d1bda6745e493dc6c50379081aff080cf63</citedby><cites>FETCH-LOGICAL-c361t-bc1b79b377db2c02c13e8f9d8acea3d1bda6745e493dc6c50379081aff080cf63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2297045326/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2297045326?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Kim, Junhong</creatorcontrib><creatorcontrib>Kim, Hyungseok</creatorcontrib><creatorcontrib>Park, Jaesun</creatorcontrib><creatorcontrib>Mo, Kyounghyun</creatorcontrib><creatorcontrib>Kang, Pilsung</creatorcontrib><title>Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification</title><title>Applied sciences</title><description>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.</description><subject>Artificial intelligence</subject><subject>bad wafer classification</subject><subject>Bin2Vec</subject><subject>Classification</subject><subject>Codes</subject><subject>Coloring</subject><subject>Competitive advantage</subject><subject>convolution neural network</subject><subject>Corporate learning</subject><subject>Decision making</subject><subject>Electronics industry</subject><subject>Engineers</subject><subject>Information processing</subject><subject>Inspection</subject><subject>International conferences</subject><subject>Learning algorithms</subject><subject>Manufacturing</subject><subject>Neural networks</subject><subject>Pattern recognition</subject><subject>Visualization</subject><subject>wafer bin map (WBM)</subject><subject>Word2Vec</subject><issn>2076-3417</issn><issn>2076-3417</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkUFP3DAQhaOqlYqAS3-Bpd4qbTuOk9jujV1BiwTiQIGjNbbH4FU2DnYW1P76BhYBc5gZPY2-N9Krqi8cvguh4QeOowYBrZYfqr0aZLcQDZcf3-2fq8NS1jCX5kJx2Ksel3Gor8n9ZEdsSdNEmd1gmPuss3Mc2Sr1Kcfhll26O9oQCynP2mbMdEdDibYndh3LFvv4D6eYBoaDZ8chkJviA7El-hfgqsdSYoju-eyg-hSwL3T4Mverq5PjP6vfi7OLX6ero7OFEx2fFtZxK7UVUnpbO6gdF6SC9godofDceuxk01KjhXeda0FIDYpjCKDAhU7sV6c7rk-4NmOOG8x_TcJonoWUbw3mKbqejKUWhWgQuPKNImVDa12D6FuoWwVyZn3dscac7rdUJrNO2zzM75u61hKaVtRPjt92Vy6nUjKFV1cO5ikn85aT-A9Wv4Va</recordid><startdate>20190211</startdate><enddate>20190211</enddate><creator>Kim, Junhong</creator><creator>Kim, Hyungseok</creator><creator>Park, Jaesun</creator><creator>Mo, Kyounghyun</creator><creator>Kang, Pilsung</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>DOA</scope></search><sort><creationdate>20190211</creationdate><title>Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification</title><author>Kim, Junhong ; Kim, Hyungseok ; Park, Jaesun ; Mo, Kyounghyun ; Kang, Pilsung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c361t-bc1b79b377db2c02c13e8f9d8acea3d1bda6745e493dc6c50379081aff080cf63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Artificial intelligence</topic><topic>bad wafer classification</topic><topic>Bin2Vec</topic><topic>Classification</topic><topic>Codes</topic><topic>Coloring</topic><topic>Competitive advantage</topic><topic>convolution neural network</topic><topic>Corporate learning</topic><topic>Decision making</topic><topic>Electronics industry</topic><topic>Engineers</topic><topic>Information processing</topic><topic>Inspection</topic><topic>International conferences</topic><topic>Learning algorithms</topic><topic>Manufacturing</topic><topic>Neural networks</topic><topic>Pattern recognition</topic><topic>Visualization</topic><topic>wafer bin map (WBM)</topic><topic>Word2Vec</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Junhong</creatorcontrib><creatorcontrib>Kim, Hyungseok</creatorcontrib><creatorcontrib>Park, Jaesun</creatorcontrib><creatorcontrib>Mo, Kyounghyun</creatorcontrib><creatorcontrib>Kang, Pilsung</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Applied sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Junhong</au><au>Kim, Hyungseok</au><au>Park, Jaesun</au><au>Mo, Kyounghyun</au><au>Kang, Pilsung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification</atitle><jtitle>Applied sciences</jtitle><date>2019-02-11</date><risdate>2019</risdate><volume>9</volume><issue>3</issue><spage>597</spage><pages>597-</pages><issn>2076-3417</issn><eissn>2076-3417</eissn><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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/app9030597</doi><oa>free_for_read</oa></addata></record> |
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subjects | Artificial intelligence bad wafer classification Bin2Vec Classification Codes Coloring Competitive advantage convolution neural network Corporate learning Decision making Electronics industry Engineers Information processing Inspection International conferences Learning algorithms Manufacturing Neural networks Pattern recognition Visualization wafer bin map (WBM) Word2Vec |
title | Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification |
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