{"id":"W3160293517","doi":"10.1109/edtm50988.2021.9420963","title":"Multi-Physics Evaluation of Silicon Steep-Slope Cold Source FET","year":2021,"lang":"en","type":"article","venue":"","topic":"Semiconductor materials and devices","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Physics; Steep slope; Topology (electrical circuits); Energy (signal processing); Silicon; Node (physics); Diffusion; Margin (machine learning); Electrical engineering; Optoelectronics; Computer science; Engineering; Thermodynamics; Quantum mechanics","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":[0.0001502136,0.0003155617,0.0002894298,0.0003771641,0.0002130772,0.0003173886,0.000558464,0.000461733,0.001977622],"category_scores_gemma":[0.000360461,0.0001381143,0.0003532137,0.0003625695,0.0001819673,0.0005301918,0.0002114865,0.0001771161,0.0002756124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008326128,"about_ca_system_score_gemma":0.0001813434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001155821,"about_ca_topic_score_gemma":0.001518299,"domain_scores_codex":[0.9999133,0.00000925811,0.000002925042,0.00001602241,0.00004476052,0.00001355827],"domain_scores_gemma":[0.9998272,0.00005195728,0.00002386672,0.00001800246,0.00006512771,0.00001397317],"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":[0.0006252408,0.0001631696,0.004353014,0.0002793057,0.000127036,0.001106596,0.0002830045,0.108454,0.8584434,0.005267833,0.001425762,0.0194716],"study_design_scores_gemma":[0.00003702477,0.001230129,0.01035345,0.00002866414,0.00007174722,0.0004632373,0.0001468374,0.7113203,0.2724415,0.001781447,0.002077192,0.00004841345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785029,0.0005233266,0.01244272,0.0001465858,0.00002653804,0.00003240044,0.0002729303,0.0003222514,0.007730406],"genre_scores_gemma":[0.9975047,0.00009469506,0.001312286,0.00001351467,0.000005891377,0.00001013724,0.0000671445,0.00002281661,0.0009689028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001977622,"threshold_uncertainty_score":0.006615818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04941176363951344,"score_gpt":0.2698384480427938,"score_spread":0.2204266844032803,"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."}}