{"id":"W3021862453","doi":"10.1002/smll.201907321","title":"Rethinking the Characterization of Nanoscale Field‐Effect Transistors: A Universal Approach","year":2020,"lang":"en","type":"article","venue":"Small","topic":"Advancements in Semiconductor Devices and Circuit Design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ontario Centres of Excellence","keywords":"Capacitance; Transistor; Field-effect transistor; Threshold voltage; Materials science; Voltage; Characterization (materials science); Hysteresis; Quantum; Conductance; Nanoscopic scale; Measure (data warehouse); Nanotechnology; Channel (broadcasting); Condensed matter physics; Optoelectronics; Physics; Computer science; Quantum mechanics; Telecommunications; Electrode","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":[0.00005774193,0.00007468962,0.0001018664,0.00001122677,0.00002710105,0.000007552006,0.0001367921,0.0000449731,0.0000388429],"category_scores_gemma":[0.000007693367,0.00005817433,0.00003860943,0.0001014204,0.00001509802,0.00006202266,0.000006938221,0.0001060549,0.000002026011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001295706,"about_ca_system_score_gemma":0.000004329903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002398525,"about_ca_topic_score_gemma":5.374583e-7,"domain_scores_codex":[0.9996257,0.0000239992,0.0001044725,0.00008911482,0.00006765792,0.00008902061],"domain_scores_gemma":[0.9998048,0.00003492221,0.00002511309,0.00009981719,0.0000104868,0.00002484467],"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":[0.00001998538,0.00001033569,0.0004116327,0.000262624,0.000053157,0.000001890616,0.00917529,0.001547496,0.9740679,0.0004712245,0.00009751511,0.01388094],"study_design_scores_gemma":[0.001854619,0.0007706824,0.001345116,0.0002185624,0.0002623568,0.000009162041,0.001047934,0.1268266,0.8222278,0.0004385506,0.0440843,0.0009142777],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8324409,0.0001484403,0.1609338,0.0001152794,0.0002371481,0.0002430969,0.000007219058,0.0001359105,0.005738271],"genre_scores_gemma":[0.9993917,0.00002392347,0.0002632782,0.0001782512,0.00008106147,0.000005134548,0.00001287274,0.00001395784,0.00002977786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1669509,"threshold_uncertainty_score":0.237228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02312677506577404,"score_gpt":0.1911875171475907,"score_spread":0.1680607420818167,"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."}}