{"id":"W2070251136","doi":"10.1116/1.1463073","title":"Dry etch process optimization for small-area<i>a</i>-Si:H vertical thin film transistor","year":2002,"lang":"en","type":"article","venue":"Journal of Vacuum Science & Technology A Vacuum Surfaces and Films","topic":"Thin-Film Transistor Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Fabrication; Dry etching; Etching (microfabrication); Thin-film transistor; Transistor; Silicon; Optoelectronics; Amorphous solid; Deposition (geology); Plasma; Plasma etching; Amorphous silicon; Thin film; Reactive-ion etching; Nanotechnology; Layer (electronics); Electrical engineering; Crystalline silicon; Chemistry; Crystallography; Geology; Engineering; Voltage","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006438499,0.0003531294,0.0005817848,0.001107181,0.0003710736,0.0001101559,0.001114477,0.0004524101,0.0000671216],"category_scores_gemma":[0.0002078154,0.0003013503,0.0001424584,0.001754,0.001076478,0.0007351977,0.0000550513,0.000756265,0.00000302871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001128634,"about_ca_system_score_gemma":0.00007806685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002251955,"about_ca_topic_score_gemma":0.00000961001,"domain_scores_codex":[0.997719,0.00001457903,0.0007376228,0.0004097493,0.0004195018,0.0006994827],"domain_scores_gemma":[0.9988337,0.0001066757,0.0001566395,0.0003630504,0.0003754701,0.000164483],"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":[0.0002300937,0.000607467,0.00849454,0.0007485506,0.0003379509,0.0001213833,0.004493476,0.708528,0.2604652,0.00248438,0.003011958,0.01047696],"study_design_scores_gemma":[0.001338241,0.0006902327,0.0006378862,0.0001715504,0.0001397055,0.0002079066,0.001680588,0.9158593,0.07583703,0.001585305,0.001274994,0.0005773085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9425306,0.005132429,0.04807465,0.002375297,0.0006587638,0.0003472768,0.00002413973,0.0007032328,0.0001536142],"genre_scores_gemma":[0.9511817,0.001013943,0.0476183,0.00005247135,0.00002330163,0.00002518071,9.854558e-7,0.00004162641,0.00004254362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2073312,"threshold_uncertainty_score":0.9999439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0159287727341057,"score_gpt":0.2191677733746007,"score_spread":0.203239000640495,"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."}}