{"id":"https://openalex.org/W3160334309","doi":"https://doi.org/10.1109/icce50685.2021.9427702","title":"Gaussian Process Regression for Small-Signal Modelling of GaN HEMTs","display_name":"Gaussian Process Regression for Small-Signal Modelling of GaN HEMTs","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3160334309","doi":"https://doi.org/10.1109/icce50685.2021.9427702","mag":"3160334309"},"language":"en","primary_location":{"id":"doi:10.1109/icce50685.2021.9427702","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce50685.2021.9427702","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053709677","display_name":"Saddam Husain","orcid":"https://orcid.org/0000-0001-9830-5133"},"institutions":[{"id":"https://openalex.org/I60559429","display_name":"Nazarbayev University","ror":"https://ror.org/052bx8q98","country_code":"KZ","type":"education","lineage":["https://openalex.org/I60559429"]}],"countries":["KZ"],"is_corresponding":true,"raw_author_name":"Saddam Husain","raw_affiliation_strings":["Nazarbayev University, Nur-Sultan, Kazakhstan"],"affiliations":[{"raw_affiliation_string":"Nazarbayev University, Nur-Sultan, Kazakhstan","institution_ids":["https://openalex.org/I60559429"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040343678","display_name":"Ahmad Khusro","orcid":"https://orcid.org/0000-0003-2025-3040"},"institutions":[{"id":"https://openalex.org/I59475050","display_name":"Jamia Millia Islamia","ror":"https://ror.org/00pnhhv55","country_code":"IN","type":"education","lineage":["https://openalex.org/I59475050"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ahmad Khusro","raw_affiliation_strings":["Jamia Millia Islamia, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"Jamia Millia Islamia, New Delhi, India","institution_ids":["https://openalex.org/I59475050"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089109424","display_name":"Mohammad Hashmi","orcid":"https://orcid.org/0000-0002-1772-588X"},"institutions":[{"id":"https://openalex.org/I60559429","display_name":"Nazarbayev University","ror":"https://ror.org/052bx8q98","country_code":"KZ","type":"education","lineage":["https://openalex.org/I60559429"]}],"countries":["KZ"],"is_corresponding":false,"raw_author_name":"Mohammad Hashmi","raw_affiliation_strings":["Nazarbayev University, Nur-Sultan, Kazakhstan"],"affiliations":[{"raw_affiliation_string":"Nazarbayev University, Nur-Sultan, Kazakhstan","institution_ids":["https://openalex.org/I60559429"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004780909","display_name":"Galymzhan Nauryzbayev","orcid":"https://orcid.org/0000-0003-4470-3851"},"institutions":[{"id":"https://openalex.org/I60559429","display_name":"Nazarbayev University","ror":"https://ror.org/052bx8q98","country_code":"KZ","type":"education","lineage":["https://openalex.org/I60559429"]}],"countries":["KZ"],"is_corresponding":false,"raw_author_name":"Galymzhan Nauryzbayev","raw_affiliation_strings":["Nazarbayev University, Nur-Sultan, Kazakhstan"],"affiliations":[{"raw_affiliation_string":"Nazarbayev University, Nur-Sultan, Kazakhstan","institution_ids":["https://openalex.org/I60559429"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019823459","display_name":"Muhammad Akmal Chaudhary","orcid":"https://orcid.org/0000-0001-6063-3377"},"institutions":[{"id":"https://openalex.org/I60559429","display_name":"Nazarbayev University","ror":"https://ror.org/052bx8q98","country_code":"KZ","type":"education","lineage":["https://openalex.org/I60559429"]}],"countries":["KZ"],"is_corresponding":false,"raw_author_name":"Muhammad Akmal Chaudhary","raw_affiliation_strings":["Nazarbayev University, Nur-Sultan, Kazakhstan"],"affiliations":[{"raw_affiliation_string":"Nazarbayev University, Nur-Sultan, Kazakhstan","institution_ids":["https://openalex.org/I60559429"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5053709677"],"corresponding_institution_ids":["https://openalex.org/I60559429"],"apc_list":null,"apc_paid":null,"fwci":0.4645,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.61919391,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10099","display_name":"GaN-based semiconductor devices and materials","score":1.0,"subfield":{"id":"https://openalex.org/subfields/3104","display_name":"Condensed Matter Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10099","display_name":"GaN-based semiconductor devices and materials","score":1.0,"subfield":{"id":"https://openalex.org/subfields/3104","display_name":"Condensed Matter Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10361","display_name":"Silicon Carbide Semiconductor Technologies","score":0.9819999933242798,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12898","display_name":"Induction Heating and Inverter Technology","score":0.9442999958992004,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6589401960372925},{"id":"https://openalex.org/keywords/high-electron-mobility-transistor","display_name":"High-electron-mobility transistor","score":0.5812791585922241},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5738663673400879},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.5734441876411438},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5653664469718933},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.48185834288597107},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.45725956559181213},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.451092004776001},{"id":"https://openalex.org/keywords/gallium-nitride","display_name":"Gallium nitride","score":0.45035749673843384},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.44403451681137085},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4399045407772064},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.4292973577976227},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4171682894229889},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.413802832365036},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4107643663883209},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3666382431983948},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32459574937820435},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2260029911994934},{"id":"https://openalex.org/keywords/transistor","display_name":"Transistor","score":0.22271665930747986},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22079986333847046},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17189407348632812},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.12345349788665771}],"concepts":[{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6589401960372925},{"id":"https://openalex.org/C162057924","wikidata":"https://www.wikidata.org/wiki/Q1617706","display_name":"High-electron-mobility transistor","level":4,"score":0.5812791585922241},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5738663673400879},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.5734441876411438},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5653664469718933},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.48185834288597107},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.45725956559181213},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.451092004776001},{"id":"https://openalex.org/C2778871202","wikidata":"https://www.wikidata.org/wiki/Q411713","display_name":"Gallium nitride","level":3,"score":0.45035749673843384},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.44403451681137085},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4399045407772064},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.4292973577976227},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4171682894229889},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.413802832365036},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4107643663883209},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3666382431983948},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32459574937820435},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2260029911994934},{"id":"https://openalex.org/C172385210","wikidata":"https://www.wikidata.org/wiki/Q5339","display_name":"Transistor","level":3,"score":0.22271665930747986},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22079986333847046},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17189407348632812},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.12345349788665771},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce50685.2021.9427702","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce50685.2021.9427702","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1746819321","https://openalex.org/W1967744582","https://openalex.org/W2078384030","https://openalex.org/W2248867217","https://openalex.org/W2514854747","https://openalex.org/W2902654293","https://openalex.org/W2919152753","https://openalex.org/W2921886617","https://openalex.org/W2947550988","https://openalex.org/W2965256140","https://openalex.org/W2998581876","https://openalex.org/W3024735379","https://openalex.org/W4211049957"],"related_works":["https://openalex.org/W2472160638","https://openalex.org/W3209950509","https://openalex.org/W2559825181","https://openalex.org/W1975307200","https://openalex.org/W4377089489","https://openalex.org/W3088454288","https://openalex.org/W2466508933","https://openalex.org/W4313611767","https://openalex.org/W2613044742","https://openalex.org/W4385217635"],"abstract_inverted_index":{"This":[0],"paper":[1],"explores":[2],"and":[3,97,123,133,138,165],"develops":[4],"a":[5,19,83],"biasdependent":[6],"small-signal":[7],"model":[8,63,70,87,117,152],"for":[9,31,46,78,120,145],"Gallium":[10],"Nitride":[11],"(GaN)":[12],"High":[13],"Electron":[14],"Mobility":[15],"Transistors":[16],"(HEMTs)":[17],"using":[18,90],"non-parametric":[20],"probability":[21],"based":[22,160,177],"Gaussian":[23],"Process":[24],"Regression":[25,168],"(GPR)":[26],"approach.":[27],"The":[28,69,116],"characterization":[29],"data":[30,94,103],"HEMT":[32],"is":[33,38,49,88,106,118,143,153],"available":[34],"in":[35,51,126],"S-parameters.":[36],"It":[37],"observed":[39],"that":[40],"the":[41,55,66,79,86,93,101,110,114,129,146,151,171,174],"determination":[42],"of":[43,92,100,113,128,136,173],"optimal":[44,67],"hyper-parameters":[45],"each":[47],"S-parameter":[48],"crucial":[50],"this":[52],"technique.":[53],"Therefore,":[54],"developed":[56,89],"modeling":[57],"technique":[58],"employs":[59],"10-fold":[60],"cross-validation":[61,76],"loss":[62],"to":[64,108,169],"find":[65],"parameters.":[68],"incorporates":[71],"Bayesian":[72],"optimization":[73],"alongside":[74],"with":[75,156],"error":[77,132],"best":[80],"outcome.":[81],"As":[82],"first":[84],"step,":[85],"two-third":[91],"(training":[95],"set)":[96,105],"subsequently":[98],"one-third":[99],"remaining":[102],"(testing":[104],"used":[107],"capture":[109],"predictive":[111],"ability":[112],"model.":[115,178],"tested":[119],"both":[121],"training":[122],"testing":[124],"sets":[125],"terms":[127],"mean":[130],"square":[131],"fitting":[134],"curves":[135],"measured":[137],"simulated":[139],"outputs.":[140],"Excellent":[141],"performance":[142],"achieved":[144],"entire":[147],"frequency":[148],"range.":[149],"Lastly,":[150],"also":[154],"compared":[155],"other":[157],"leading":[158],"models":[159],"on":[161],"Artificial":[162],"Neural":[163],"Network":[164],"Support":[166],"Vector":[167],"demonstrate":[170],"effectiveness":[172],"proposed":[175],"GPR":[176]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}