{"id":"W2564119593","doi":"10.1109/led.2016.2641740","title":"Characterization of Dynamic Self-Heating in GaN HEMTs Using Gate Resistance Measurement","year":2016,"lang":"en","type":"article","venue":"IEEE Electron Device Letters","topic":"GaN-based semiconductor devices and materials","field":"Physics and Astronomy","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Direction Générale de l’Armement; Natural Sciences and Engineering Research Council of Canada; LabEx GANEX; Agence Nationale de la Recherche","keywords":"Materials science; Transistor; Optoelectronics; Transient (computer programming); Thermal resistance; Substrate (aquarium); Electrical impedance; Power semiconductor device; Voltage; Electrical engineering; Electronic engineering; Thermal; Computer science; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003149167,0.0001795163,0.0002552214,0.00009693493,0.00004660187,0.00002562078,0.0001367867,0.00003302672,0.00004268886],"category_scores_gemma":[0.000003587966,0.0001510777,0.00005762421,0.0001701267,0.00001944834,0.000214171,0.000008539238,0.00007430515,0.00000702766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002170061,"about_ca_system_score_gemma":0.00006282666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001371983,"about_ca_topic_score_gemma":0.00008278684,"domain_scores_codex":[0.9985885,0.00009928851,0.000410904,0.0002768304,0.000233152,0.0003913363],"domain_scores_gemma":[0.9993502,0.00002505037,0.0003051781,0.000206513,0.00007168839,0.000041313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002070304,0.000034932,0.004236222,0.00005741234,0.00003178144,8.632621e-7,0.0001245843,0.00002183754,0.9950862,0.00004640218,0.00001016229,0.0003288909],"study_design_scores_gemma":[0.0005715573,0.00001769304,0.004406072,0.000329496,0.00002786524,3.721475e-7,0.00001713237,0.000139149,0.9939427,0.00006169595,0.0002743074,0.0002119197],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968929,0.00002183782,0.001981173,0.0005778139,0.0001914703,0.0002316754,0.00001720916,0.00002937343,0.00005653768],"genre_scores_gemma":[0.999241,0.000003592267,0.000276225,0.0002794432,0.0001234149,0.00001660994,0.00001142039,0.00002890928,0.00001938361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002348092,"threshold_uncertainty_score":0.6160768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01449301377629606,"score_gpt":0.2381361637581325,"score_spread":0.2236431499818364,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}