{"id":"W3097308268","doi":"10.1109/jeds.2020.3035628","title":"Large-Signal Modeling of GaN HEMTs Using Hybrid GA-ANN, PSO-SVR, and GPR-Based Approaches","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of the Electron Devices Society","topic":"GaN-based semiconductor devices and materials","field":"Physics and Astronomy","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Particle swarm optimization; Backpropagation; Artificial neural network; Support vector machine; Kriging; Genetic algorithm; Computer science; Convergence (economics); Maxima and minima; SIGNAL (programming language); Amplifier; High-electron-mobility transistor; Rate of convergence; Algorithm; Electronic engineering; Engineering; Artificial intelligence; Machine learning; Mathematics; Transistor; Voltage","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004802304,0.0002202527,0.0004683691,0.00002209067,0.0001672657,0.0000772303,0.0003514396,0.00004954743,0.00005663248],"category_scores_gemma":[0.000004318341,0.0001576775,0.0004858091,0.0001380734,0.0000527439,0.0002164285,0.00003640848,0.0003478075,6.199895e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000389317,"about_ca_system_score_gemma":0.0002423873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003683145,"about_ca_topic_score_gemma":0.000001980805,"domain_scores_codex":[0.9984631,0.0001170283,0.0005683516,0.0001903886,0.0003138117,0.0003473304],"domain_scores_gemma":[0.9987414,0.00005075116,0.0007855723,0.0001484466,0.0001434568,0.0001303762],"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":[0.000117671,0.0001855556,0.009093192,0.000308598,0.0006811185,0.000001465305,0.002034914,0.06115591,0.9253811,0.0001077494,0.0007142824,0.000218397],"study_design_scores_gemma":[0.001460914,0.0001878308,0.00009996222,0.0001601708,0.0004240477,0.00001146435,0.001622671,0.2692684,0.7254977,0.0005796618,0.0003947952,0.0002923046],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899199,0.000704427,0.008457799,0.0005274895,0.0001624258,0.0001305414,0.00004838248,0.0000085206,0.00004048932],"genre_scores_gemma":[0.9979417,0.000007621494,0.000847814,0.000434937,0.0007289922,0.000001199336,0.000003308219,0.00002847025,0.000005920093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2081125,"threshold_uncertainty_score":0.6429902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05346290765596329,"score_gpt":0.2578077794155523,"score_spread":0.204344871759589,"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."}}