{"id":"W2107985063","doi":"10.1109/ted.2003.818156","title":"Above-threshold parameter extraction and modeling for amorphous silicon thin-film transistors","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Electron Devices","topic":"Thin-Film Transistor Technologies","field":"Engineering","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thin-film transistor; Amorphous silicon; Materials science; Threshold voltage; Optoelectronics; Transistor; Extraction (chemistry); Saturation (graph theory); Amorphous solid; Fabrication; Silicon; Electron mobility; Band gap; Contact resistance; Oxide thin-film transistor; Electronic engineering; Voltage; Layer (electronics); Crystalline silicon; Electrical engineering; Nanotechnology; Engineering","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":[0.0001673606,0.0006401608,0.0004648771,0.0004581672,0.0002904318,0.0004900038,0.0009552081,0.0006734629,0.001006132],"category_scores_gemma":[0.0007406842,0.0003143157,0.0006963561,0.0003451002,0.0002515065,0.0008958529,0.0002144188,0.0004730397,0.0004767898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006454819,"about_ca_system_score_gemma":0.00052393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003148509,"about_ca_topic_score_gemma":0.002749525,"domain_scores_codex":[0.9998516,0.00001672469,0.000009462436,0.00002092475,0.00008717748,0.00001421106],"domain_scores_gemma":[0.9998521,0.00006814887,0.00002195759,0.00002314058,0.00002958622,0.000005147609],"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":[0.00005829464,0.00003836892,0.001034832,0.0002059775,0.00005021068,0.0004396475,0.0001704013,0.8181742,0.1453687,0.01175679,0.0006950375,0.02200748],"study_design_scores_gemma":[0.000006067711,0.00003208833,0.0002937418,0.00001198292,0.00001322995,0.00009622017,0.00001804554,0.9737483,0.02024065,0.003436614,0.002093423,0.000009612308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1154871,0.001611357,0.8735688,0.0001943486,0.00005170957,0.0001149895,0.0007369527,0.001225908,0.007008776],"genre_scores_gemma":[0.9049453,0.002032403,0.08653183,0.00005518056,0.00004576348,0.0002578984,0.0006695952,0.0002202298,0.005241842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003148509,"threshold_uncertainty_score":0.006260335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792891490317274,"score_gpt":0.2411192091563888,"score_spread":0.223190294253216,"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."}}