{"id":"W1842444622","doi":"10.1002/jnm.2100","title":"Efficient modeling of GaN HEMTs for linear and nonlinear circuits design","year":2015,"lang":"en","type":"article","venue":"International Journal of Numerical Modelling Electronic Networks Devices and Fields","topic":"GaN-based semiconductor devices and materials","field":"Physics and Astronomy","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Universität Kassel","keywords":"High-electron-mobility transistor; Amplifier; Transistor; Gallium nitride; Nonlinear system; Substrate (aquarium); Electronic engineering; SIGNAL (programming language); Electronic circuit; Large-signal model; Materials science; Optoelectronics; Computer science; Electrical engineering; Physics; Engineering; Nanotechnology; Voltage; Layer (electronics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001186401,0.000478967,0.0002411548,0.0001631223,0.0001519619,0.0003610412,0.0005675142,0.0004244611,0.001926391],"category_scores_gemma":[0.000238038,0.0001805644,0.0004456084,0.000143706,0.0001572979,0.0003768338,0.000214192,0.0003383213,0.0005677836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004455228,"about_ca_system_score_gemma":0.0003090008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001742264,"about_ca_topic_score_gemma":0.00229781,"domain_scores_codex":[0.9999197,0.00002154482,0.000002521665,0.00001141611,0.00003576311,0.000009113704],"domain_scores_gemma":[0.9999403,0.0000217555,0.000007680906,0.00001040199,0.00001707076,0.000002874815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002755188,0.00001922492,0.0004177453,0.00008744688,0.00003304505,0.0001058265,0.00004616026,0.9268793,0.04052539,0.01453988,0.0009697296,0.01634876],"study_design_scores_gemma":[0.000001353435,0.000007903652,0.0000627989,0.000002199515,0.000002842675,0.00001443842,0.000002663912,0.9962273,0.00173335,0.0007997344,0.001144135,0.000001409402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0390913,0.0006150283,0.9390938,0.0001561111,0.00004844604,0.00006797975,0.0002042995,0.0007264141,0.01999668],"genre_scores_gemma":[0.8580461,0.0007801001,0.1247754,0.00007363001,0.00004688249,0.0002052531,0.0002682198,0.0001841808,0.01562011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001926391,"threshold_uncertainty_score":0.006444395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03857199393896264,"score_gpt":0.2796686270324663,"score_spread":0.2410966330935037,"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."}}