Nonlinear AlGaN/GaN HEMT model using multiple artificial neural networks
In this work, a complete nonlinear-transistor-model extraction-method is described. As a case study, the AlGaN/GaN High Electron Mobility Transistor manufactured on SiC substrate is modeled. The parasitic components model is proposed, and its extraction results are presented. Low- and high-frequency...
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| creator | Barmuta, P. Plonski, P. Czuba, K. Avolio, G. Schreurs, D. |
| description | In this work, a complete nonlinear-transistor-model extraction-method is described. As a case study, the AlGaN/GaN High Electron Mobility Transistor manufactured on SiC substrate is modeled. The parasitic components model is proposed, and its extraction results are presented. Low- and high-frequency large-signal measurement data are involved in order to produce multiple artificial neural networks. The network topologies of multilayer perceptron networks are established automatically. A complete learning procedure using back propagation algorithm is described. A good agreement between the measurement data and the model has been observed. |
| doi_str_mv | 10.1109/MIKON.2012.6233556 |
| format | Conference Proceeding |
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A good agreement between the measurement data and the model has been observed.</description><subject>artificial neural network</subject><subject>Decision support systems</subject><subject>GaN HEMT</subject><subject>nonlinear model</subject><subject>temperature</subject><isbn>9781457714351</isbn><isbn>1457714353</isbn><isbn>145771437X</isbn><isbn>1457714388</isbn><isbn>9781457714375</isbn><isbn>9781457714382</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j8tKw0AYhUdEUGteQDfzAknnn0smsyylNsU23VRwVybJPzI6SUouiG9v0Xrg8PFtDhxCHoElAMzMd5uXfZFwBjxJuRBKpVfkHqTSGqTQb9ckMjr7dwW3JBqGD3aOVqB1dkfyomuDb9H2dBHWtpifS_PV7kCbrsZAp8G377SZwuhPAantR-985W2gLU79L8avrv8cHsiNs2HA6MIZeX1eHZZ5vN2vN8vFNvag1Rg7o5lS0gFYMK5GUQJnLLNW6lJWjoNCKRV3UBssJUoQqWJZmkonsBLciBl5-tv1iHg89b6x_ffxcl78AFyWTJM</recordid><startdate>201205</startdate><enddate>201205</enddate><creator>Barmuta, P.</creator><creator>Plonski, P.</creator><creator>Czuba, K.</creator><creator>Avolio, G.</creator><creator>Schreurs, D.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201205</creationdate><title>Nonlinear AlGaN/GaN HEMT model using multiple artificial neural networks</title><author>Barmuta, P. ; Plonski, P. ; Czuba, K. ; Avolio, G. ; Schreurs, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-f970554f11a19fde3b12008aa47b4cf215e4452f1d9eb4e4136508664f3ec3293</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>artificial neural network</topic><topic>Decision support systems</topic><topic>GaN HEMT</topic><topic>nonlinear model</topic><topic>temperature</topic><toplevel>online_resources</toplevel><creatorcontrib>Barmuta, P.</creatorcontrib><creatorcontrib>Plonski, P.</creatorcontrib><creatorcontrib>Czuba, K.</creatorcontrib><creatorcontrib>Avolio, G.</creatorcontrib><creatorcontrib>Schreurs, D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Barmuta, P.</au><au>Plonski, P.</au><au>Czuba, K.</au><au>Avolio, G.</au><au>Schreurs, D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Nonlinear AlGaN/GaN HEMT model using multiple artificial neural networks</atitle><btitle>2012 19th International Conference on Microwaves, Radar & Wireless Communications</btitle><stitle>MIKON</stitle><date>2012-05</date><risdate>2012</risdate><volume>2</volume><spage>462</spage><epage>466</epage><pages>462-466</pages><isbn>9781457714351</isbn><isbn>1457714353</isbn><eisbn>145771437X</eisbn><eisbn>1457714388</eisbn><eisbn>9781457714375</eisbn><eisbn>9781457714382</eisbn><abstract>In this work, a complete nonlinear-transistor-model extraction-method is described. As a case study, the AlGaN/GaN High Electron Mobility Transistor manufactured on SiC substrate is modeled. The parasitic components model is proposed, and its extraction results are presented. Low- and high-frequency large-signal measurement data are involved in order to produce multiple artificial neural networks. The network topologies of multilayer perceptron networks are established automatically. A complete learning procedure using back propagation algorithm is described. A good agreement between the measurement data and the model has been observed.</abstract><pub>IEEE</pub><doi>10.1109/MIKON.2012.6233556</doi><tpages>5</tpages></addata></record> |
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| subjects | artificial neural network Decision support systems GaN HEMT nonlinear model temperature |
| title | Nonlinear AlGaN/GaN HEMT model using multiple artificial neural networks |
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