{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000126149,0.0002416128,0.0001747937,0.0002380936,0.00008539087,0.0002263139,0.0003317255,0.0002066698,0.0004251378],"category_scores_gemma":[0.0002925271,0.00009888456,0.0001570279,0.0001448649,0.0002493742,0.000281279,0.0001366243,0.0002083568,0.0001429553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002014977,"about_ca_system_score_gemma":0.00005959589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002602519,"about_ca_topic_score_gemma":0.0004326748,"domain_scores_codex":[0.9998636,0.00001706143,0.000005700558,0.00003849541,0.00005756399,0.0000175675],"domain_scores_gemma":[0.9998088,0.00007102021,0.00003307494,0.00004333783,0.00003427965,0.000009365772],"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":[0.00003181595,0.000009307922,0.001139834,0.00002147794,0.000007152807,0.00007036948,0.00005004924,0.0006713862,0.9935376,0.0001225531,0.00003089348,0.004307613],"study_design_scores_gemma":[0.000003973722,0.0001745022,0.01082975,0.000003873302,0.00001048425,0.0002514546,0.0000412664,0.01945941,0.9685115,0.000113233,0.0005913718,0.000009265916],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668577,0.0002765309,0.03076404,0.0000324475,0.00002030437,0.00002588011,0.0001488213,0.0002498151,0.001624437],"genre_scores_gemma":[0.9968234,0.00007603062,0.002700773,0.0000072976,0.000004549455,0.00000844626,0.00003826208,0.0000149372,0.0003263007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004251378,"threshold_uncertainty_score":0.001461923,"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."}}