{"id":"W3043253403","doi":"10.1109/ted.2020.3007598","title":"Numerical Solutions for Electric Field Lines and Breakdown Voltages in Superjunction-Like Power Devices","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Electron Devices","topic":"Silicon Carbide Semiconductor Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; National Natural Science Foundation of China","keywords":"Electric field; Breakdown voltage; Impact ionization; Voltage; High voltage; Insulator (electricity); Pillar; Electronic engineering; Electrical engineering; Computational physics; Field (mathematics); Physics; Computer science; Ionization; Optoelectronics; Engineering; Mathematics; Mechanical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006526076,0.0002647144,0.0002857743,0.000289279,0.0001320957,0.00005562579,0.0001894689,0.0002148585,0.00006403319],"category_scores_gemma":[0.00002412789,0.0002713475,0.00009587681,0.0007099827,0.00002573998,0.000299122,0.000001594971,0.0004698476,0.0000146934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008069172,"about_ca_system_score_gemma":0.00003305518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008236194,"about_ca_topic_score_gemma":0.0005683882,"domain_scores_codex":[0.998652,0.00002083924,0.0002905733,0.0003622068,0.0001272311,0.0005471964],"domain_scores_gemma":[0.9993404,0.0003255698,0.00003017296,0.0001725601,0.00004460171,0.00008671243],"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":[0.0002121854,0.0001929717,0.002084166,0.0003709255,0.0003411756,0.000007388306,0.0006995699,0.0571171,0.9006652,0.0001957029,0.004306396,0.0338072],"study_design_scores_gemma":[0.001178895,0.001330716,0.001698802,0.00006467429,0.0001285168,0.00003551386,0.0007402853,0.1797386,0.8061913,0.0001517648,0.007834041,0.0009069183],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9388863,0.003201373,0.05333226,0.002135073,0.0004446117,0.0005116595,0.00001748575,0.001329343,0.0001418178],"genre_scores_gemma":[0.9985465,0.0003313573,0.0001423553,0.0006749926,0.00005234381,0.0001798332,0.000002601567,0.00004571691,0.00002423506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1226215,"threshold_uncertainty_score":0.9999739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01614429311194355,"score_gpt":0.2334603014321196,"score_spread":0.2173160083201761,"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."}}