Structural Feature-Based Fault-Detection Approach for the Recipes of Similar Products
The sensor signals (i.e., data streams of process parameters) of semiconductor processes exhibit nonlinear, multimodal trajectories with some common structural features. In this paper, we propose a process fault-detection approach based on the structural features of the sensor signals, such as the g...
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| Veröffentlicht in: | IEEE transactions on semiconductor manufacturing 2010-05, Vol.23 (2), p.273-283 |
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| creator | Ko, Jong Myoung Kim, Chang Ouk Lee, Seung Jun Hong, Joo Pyo |
| description | The sensor signals (i.e., data streams of process parameters) of semiconductor processes exhibit nonlinear, multimodal trajectories with some common structural features. In this paper, we propose a process fault-detection approach based on the structural features of the sensor signals, such as the geometric shape, length, and height. The approach aims at constructing a shared univariate model and a multivariate model. The shared univariate model is set up for individual process parameters and clusters the process recipes of similar products. The result is a tree where the leaf nodes and intermediate nodes correspond to individual recipes and feature-based fault-detection criteria, respectively. The recipes with the same parent nodes share the criteria specified in the nodes. On the other hand, the multivariate model is constructed for a process recipe. It builds a Hotelling's T 2 that considers the correlations between the signal structures of the process parameters. We demonstrated that the test results of the two models using the data collected from a work-site etch process were encouraging. |
| doi_str_mv | 10.1109/TSM.2010.2045587 |
| format | Article |
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In this paper, we propose a process fault-detection approach based on the structural features of the sensor signals, such as the geometric shape, length, and height. The approach aims at constructing a shared univariate model and a multivariate model. The shared univariate model is set up for individual process parameters and clusters the process recipes of similar products. The result is a tree where the leaf nodes and intermediate nodes correspond to individual recipes and feature-based fault-detection criteria, respectively. The recipes with the same parent nodes share the criteria specified in the nodes. On the other hand, the multivariate model is constructed for a process recipe. It builds a Hotelling's T 2 that considers the correlations between the signal structures of the process parameters. We demonstrated that the test results of the two models using the data collected from a work-site etch process were encouraging.</description><identifier>ISSN: 0894-6507</identifier><identifier>EISSN: 1558-2345</identifier><identifier>DOI: 10.1109/TSM.2010.2045587</identifier><identifier>CODEN: ITSMED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Construction ; Criteria ; Electronics ; Etching ; Exact sciences and technology ; Fault detection ; Feature extraction ; Feature-based fault-detection criteria ; General equipment and techniques ; Instruments, apparatus, components and techniques common to several branches of physics and astronomy ; Microelectronic fabrication (materials and surfaces technology) ; Multimodal sensors ; multivariate model ; Physics ; process fault detection ; Process parameters ; Recipes ; Semiconductor device modeling ; Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices ; semiconductor manufacturing ; Semiconductor process modeling ; Semiconductors ; Sensor phenomena and characterization ; Sensors ; Sensors (chemical, optical, electrical, movement, gas, etc.); remote sensing ; Shape control ; shared univariate model ; Signal processing ; Statistics ; Streams ; Studies ; Temperature sensors ; Testing, measurement, noise and reliability</subject><ispartof>IEEE transactions on semiconductor manufacturing, 2010-05, Vol.23 (2), p.273-283</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) May 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c387t-af23475316d5c3f9c794349db68b28a11974bbcbe75fc27ca7569c4d945860543</citedby><cites>FETCH-LOGICAL-c387t-af23475316d5c3f9c794349db68b28a11974bbcbe75fc27ca7569c4d945860543</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5431000$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5431000$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22795925$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Ko, Jong Myoung</creatorcontrib><creatorcontrib>Kim, Chang Ouk</creatorcontrib><creatorcontrib>Lee, Seung Jun</creatorcontrib><creatorcontrib>Hong, Joo Pyo</creatorcontrib><title>Structural Feature-Based Fault-Detection Approach for the Recipes of Similar Products</title><title>IEEE transactions on semiconductor manufacturing</title><addtitle>TSM</addtitle><description>The sensor signals (i.e., data streams of process parameters) of semiconductor processes exhibit nonlinear, multimodal trajectories with some common structural features. In this paper, we propose a process fault-detection approach based on the structural features of the sensor signals, such as the geometric shape, length, and height. The approach aims at constructing a shared univariate model and a multivariate model. The shared univariate model is set up for individual process parameters and clusters the process recipes of similar products. The result is a tree where the leaf nodes and intermediate nodes correspond to individual recipes and feature-based fault-detection criteria, respectively. The recipes with the same parent nodes share the criteria specified in the nodes. On the other hand, the multivariate model is constructed for a process recipe. It builds a Hotelling's T 2 that considers the correlations between the signal structures of the process parameters. We demonstrated that the test results of the two models using the data collected from a work-site etch process were encouraging.</description><subject>Applied sciences</subject><subject>Construction</subject><subject>Criteria</subject><subject>Electronics</subject><subject>Etching</subject><subject>Exact sciences and technology</subject><subject>Fault detection</subject><subject>Feature extraction</subject><subject>Feature-based fault-detection criteria</subject><subject>General equipment and techniques</subject><subject>Instruments, apparatus, components and techniques common to several branches of physics and astronomy</subject><subject>Microelectronic fabrication (materials and surfaces technology)</subject><subject>Multimodal sensors</subject><subject>multivariate model</subject><subject>Physics</subject><subject>process fault detection</subject><subject>Process parameters</subject><subject>Recipes</subject><subject>Semiconductor device modeling</subject><subject>Semiconductor electronics. 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Solid state devices</subject><subject>semiconductor manufacturing</subject><subject>Semiconductor process modeling</subject><subject>Semiconductors</subject><subject>Sensor phenomena and characterization</subject><subject>Sensors</subject><subject>Sensors (chemical, optical, electrical, movement, gas, etc.); remote sensing</subject><subject>Shape control</subject><subject>shared univariate model</subject><subject>Signal processing</subject><subject>Statistics</subject><subject>Streams</subject><subject>Studies</subject><subject>Temperature sensors</subject><subject>Testing, measurement, noise and reliability</subject><issn>0894-6507</issn><issn>1558-2345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqFkc1Lw0AQxRdRsFbvgpcFEbxE9_vjWKtVoaLY9hw2mwlNSZu6mxz8793S4sGLp5lhfvN4w0PokpI7Som9n8_e7hhJEyNCSqOP0ICmmjEu5DEaEGNFpiTRp-gsxhUhVAirB2gx60Lvuz64Bk_ApQayBxehxBPXN132CB34rm43eLTdhtb5Ja7agLsl4E_w9RYibis8q9d14wL-CG2Z1OI5OqlcE-HiUIdoMXmaj1-y6fvz63g0zTw3ustcldxpyakqpeeV9doKLmxZKFMw4yi1WhSFL0DLyjPtnZbKelFaIY0iUvAhut3rJmtfPcQuX9fRQ9O4DbR9zKnSlBsjqP0f5VQqI4mSCb3-g67aPmzSIzklTFOtBFWJInvKhzbGAFW-DfXahe8E5btI8hRJvoskP0SSTm4Owi5611TBbXwdf-8Y01ZatjNwtedqAPhdp38pIYT_AKrXkkU</recordid><startdate>20100501</startdate><enddate>20100501</enddate><creator>Ko, Jong Myoung</creator><creator>Kim, Chang Ouk</creator><creator>Lee, Seung Jun</creator><creator>Hong, Joo Pyo</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Applied sciences Construction Criteria Electronics Etching Exact sciences and technology Fault detection Feature extraction Feature-based fault-detection criteria General equipment and techniques Instruments, apparatus, components and techniques common to several branches of physics and astronomy Microelectronic fabrication (materials and surfaces technology) Multimodal sensors multivariate model Physics process fault detection Process parameters Recipes Semiconductor device modeling Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices semiconductor manufacturing Semiconductor process modeling Semiconductors Sensor phenomena and characterization Sensors Sensors (chemical, optical, electrical, movement, gas, etc.) remote sensing Shape control shared univariate model Signal processing Statistics Streams Studies Temperature sensors Testing, measurement, noise and reliability |
| title | Structural Feature-Based Fault-Detection Approach for the Recipes of Similar Products |
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