{"id":"W2079229314","doi":"10.1049/ip-smt:20040152","title":"Enhanced empirical large-signal model for HBTs with performance comparable with physics-based models","year":2004,"lang":"en","type":"article","venue":"IEE Proceedings - Science Measurement and Technology","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure","funders":"","keywords":"Large-signal model; Heterojunction bipolar transistor; SIGNAL (programming language); Common emitter; Bipolar junction transistor; Empirical modelling; Small-signal model; Transistor; Range (aeronautics); Physics; Computational physics; Mode (computer interface); Electronic engineering; Voltage; Optoelectronics; Computer science; Materials science; Power (physics); Engineering; Simulation","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":[0.0004913636,0.0002836938,0.0002809946,0.0003345303,0.0003347399,0.00009605916,0.0004291809,0.0001194146,0.0000011141],"category_scores_gemma":[0.00001462654,0.0002175645,0.00002001292,0.001397287,0.0006242831,0.0007493398,0.000005730609,0.0002775893,0.000001984697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003979823,"about_ca_system_score_gemma":0.0003854805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001742701,"about_ca_topic_score_gemma":0.000007686735,"domain_scores_codex":[0.99787,0.000001130061,0.0002014296,0.0005086649,0.0006704025,0.0007483202],"domain_scores_gemma":[0.9988477,0.000004886382,0.00006388893,0.0001451323,0.0008308548,0.000107547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005466996,0.00008432069,0.0006998022,0.0001223955,0.00002855399,9.229693e-7,0.0004352704,0.4539813,0.5312964,0.01197813,0.00003396363,0.001284241],"study_design_scores_gemma":[0.0009046718,0.0002968282,0.00001447733,0.0001145849,0.0000178737,0.000008475092,0.00007534328,0.5241193,0.466508,0.007729753,0.000005887545,0.000204826],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4487288,0.00007417874,0.5480251,0.0001350711,0.0000240451,0.0004589382,0.000002284879,0.0004932994,0.002058293],"genre_scores_gemma":[0.9891849,0.00000870475,0.01038287,0.00004408762,0.00001817531,0.0003195345,7.056044e-7,0.00003384073,0.00000724823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.540456,"threshold_uncertainty_score":0.8872024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04989658764561449,"score_gpt":0.2361067970620649,"score_spread":0.1862102094164504,"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."}}