Optimization on Adhesive Stamp Mass-Transfer of Micro-LEDs With Support Vector Machine Model
In this work, the process of adhesive stamp mass-transfer of micro light-emitting diode (micro-LED) is optimized by a Support Vector Machine (SVM) model. The pick-up experiments have been performed repeatedly for hundreds of times from which the separation speed and the force between the stamp and t...
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| Veröffentlicht in: | IEEE journal of the Electron Devices Society 2020, Vol.8, p.554-558 |
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| container_title | IEEE journal of the Electron Devices Society |
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| creator | Lu, Hao Guo, Weijie Su, Changwen Li, Xilong Lu, Yijun Chen, Zhong Zhu, Lihong |
| description | In this work, the process of adhesive stamp mass-transfer of micro light-emitting diode (micro-LED) is optimized by a Support Vector Machine (SVM) model. The pick-up experiments have been performed repeatedly for hundreds of times from which the separation speed and the force between the stamp and the donor substrate are extracted as signal features. The SVM model with a Gaussian kernel function is designed to classify pick-up results into success and failure. In addition, the optimal cost parameter C as well as the Gaussian kernel function parameter gamma (\gamma) has been optimized, leading to the improvement of the classification by Particle Swarm Optimization (PSO) algorithm. Finally, an 85% classification accuracy is achieved based on the SVM model, implying that more sophisticated definition of signal features is demanded in future work. |
| doi_str_mv | 10.1109/JEDS.2020.2995710 |
| format | Article |
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The pick-up experiments have been performed repeatedly for hundreds of times from which the separation speed and the force between the stamp and the donor substrate are extracted as signal features. The SVM model with a Gaussian kernel function is designed to classify pick-up results into success and failure. In addition, the optimal cost parameter <inline-formula> <tex-math notation="LaTeX">C </tex-math></inline-formula> as well as the Gaussian kernel function parameter gamma <inline-formula> <tex-math notation="LaTeX">(\gamma) </tex-math></inline-formula> has been optimized, leading to the improvement of the classification by Particle Swarm Optimization (PSO) algorithm. 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(IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c402t-180c393d28ccd2d34cd3c76534f98c9ede769ca3d342cd1c44c07df18480fad3</citedby><cites>FETCH-LOGICAL-c402t-180c393d28ccd2d34cd3c76534f98c9ede769ca3d342cd1c44c07df18480fad3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9096346$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Lu, Hao</creatorcontrib><creatorcontrib>Guo, Weijie</creatorcontrib><creatorcontrib>Su, Changwen</creatorcontrib><creatorcontrib>Li, Xilong</creatorcontrib><creatorcontrib>Lu, Yijun</creatorcontrib><creatorcontrib>Chen, Zhong</creatorcontrib><creatorcontrib>Zhu, Lihong</creatorcontrib><title>Optimization on Adhesive Stamp Mass-Transfer of Micro-LEDs With Support Vector Machine Model</title><title>IEEE journal of the Electron Devices Society</title><addtitle>JEDS</addtitle><description><![CDATA[In this work, the process of adhesive stamp mass-transfer of micro light-emitting diode (micro-LED) is optimized by a Support Vector Machine (SVM) model. The pick-up experiments have been performed repeatedly for hundreds of times from which the separation speed and the force between the stamp and the donor substrate are extracted as signal features. The SVM model with a Gaussian kernel function is designed to classify pick-up results into success and failure. In addition, the optimal cost parameter <inline-formula> <tex-math notation="LaTeX">C </tex-math></inline-formula> as well as the Gaussian kernel function parameter gamma <inline-formula> <tex-math notation="LaTeX">(\gamma) </tex-math></inline-formula> has been optimized, leading to the improvement of the classification by Particle Swarm Optimization (PSO) algorithm. Finally, an 85% classification accuracy is achieved based on the SVM model, implying that more sophisticated definition of signal features is demanded in future work.]]></description><subject>Adhesive stamp</subject><subject>Algorithms</subject><subject>Classification</subject><subject>Feature extraction</subject><subject>Force</subject><subject>Kernel</subject><subject>Kernel functions</subject><subject>Light emitting diodes</subject><subject>mass-transfer</subject><subject>Mathematical model</subject><subject>Mathematical models</subject><subject>micro-LEDs</subject><subject>Parameters</subject><subject>Particle swarm optimization</subject><subject>Printing</subject><subject>Substrates</subject><subject>support vector machine model</subject><subject>Support vector machines</subject><issn>2168-6734</issn><issn>2168-6734</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUV1rWzEMvYwNWrr-gNIXw55v6q9rXz-WJOs6EvqQsL4UjCvbi0MS39lOofv1dZYQJgQS0tGR0GmaG4JHhGB193M6WYwopnhEleokwZ-aS0pE3wrJ-Of_8ovmOuc1rtYToYS4bF6ehhK24a8pIe5Q9Xu7cjm8ObQoZjugucm5XSazy94lFD2aB0ixnU0nGT2HskKL_TDEVNAvByWmiodV2Dk0j9ZtvjZfvNlkd32KV83y-3Q5_tHOnh4ex_ezFjimpSU9BqaYpT2ApZZxsAyk6Bj3qgflrJNCgWG1Q8ES4BywtJ70vMfeWHbVPB5pbTRrPaSwNeldRxP0v0JMv7VJJcDGafHqKSHSMC8Up4opAxgkSOWo6DrZVa5vR64hxT97l4tex33a1es15QR3dSnpK4ocUfUXOSfnz1sJ1gdJ9EESfZBEnySpM7fHmeCcO-MVVoJxwT4ARZ2GBg</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Lu, Hao</creator><creator>Guo, Weijie</creator><creator>Su, Changwen</creator><creator>Li, Xilong</creator><creator>Lu, Yijun</creator><creator>Chen, Zhong</creator><creator>Zhu, Lihong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>DOA</scope></search><sort><creationdate>2020</creationdate><title>Optimization on Adhesive Stamp Mass-Transfer of Micro-LEDs With Support Vector Machine Model</title><author>Lu, Hao ; Guo, Weijie ; Su, Changwen ; Li, Xilong ; Lu, Yijun ; Chen, Zhong ; Zhu, Lihong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c402t-180c393d28ccd2d34cd3c76534f98c9ede769ca3d342cd1c44c07df18480fad3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adhesive stamp</topic><topic>Algorithms</topic><topic>Classification</topic><topic>Feature extraction</topic><topic>Force</topic><topic>Kernel</topic><topic>Kernel functions</topic><topic>Light emitting diodes</topic><topic>mass-transfer</topic><topic>Mathematical model</topic><topic>Mathematical models</topic><topic>micro-LEDs</topic><topic>Parameters</topic><topic>Particle swarm optimization</topic><topic>Printing</topic><topic>Substrates</topic><topic>support vector machine model</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lu, Hao</creatorcontrib><creatorcontrib>Guo, Weijie</creatorcontrib><creatorcontrib>Su, Changwen</creatorcontrib><creatorcontrib>Li, Xilong</creatorcontrib><creatorcontrib>Lu, Yijun</creatorcontrib><creatorcontrib>Chen, Zhong</creatorcontrib><creatorcontrib>Zhu, Lihong</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE journal of the Electron Devices Society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lu, Hao</au><au>Guo, Weijie</au><au>Su, Changwen</au><au>Li, Xilong</au><au>Lu, Yijun</au><au>Chen, Zhong</au><au>Zhu, Lihong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimization on Adhesive Stamp Mass-Transfer of Micro-LEDs With Support Vector Machine Model</atitle><jtitle>IEEE journal of the Electron Devices Society</jtitle><stitle>JEDS</stitle><date>2020</date><risdate>2020</risdate><volume>8</volume><spage>554</spage><epage>558</epage><pages>554-558</pages><issn>2168-6734</issn><eissn>2168-6734</eissn><coden>IJEDAC</coden><abstract><![CDATA[In this work, the process of adhesive stamp mass-transfer of micro light-emitting diode (micro-LED) is optimized by a Support Vector Machine (SVM) model. The pick-up experiments have been performed repeatedly for hundreds of times from which the separation speed and the force between the stamp and the donor substrate are extracted as signal features. The SVM model with a Gaussian kernel function is designed to classify pick-up results into success and failure. In addition, the optimal cost parameter <inline-formula> <tex-math notation="LaTeX">C </tex-math></inline-formula> as well as the Gaussian kernel function parameter gamma <inline-formula> <tex-math notation="LaTeX">(\gamma) </tex-math></inline-formula> has been optimized, leading to the improvement of the classification by Particle Swarm Optimization (PSO) algorithm. Finally, an 85% classification accuracy is achieved based on the SVM model, implying that more sophisticated definition of signal features is demanded in future work.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JEDS.2020.2995710</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Adhesive stamp Algorithms Classification Feature extraction Force Kernel Kernel functions Light emitting diodes mass-transfer Mathematical model Mathematical models micro-LEDs Parameters Particle swarm optimization Printing Substrates support vector machine model Support vector machines |
| title | Optimization on Adhesive Stamp Mass-Transfer of Micro-LEDs With Support Vector Machine Model |
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