{"id":"W4313179791","doi":"10.1109/pn56061.2022.9908358","title":"Data-Driven Prediction of Fabrication Variations in Silicon Photonic Devices","year":2022,"lang":"en","type":"article","venue":"2022 Photonics North (PN)","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"","keywords":"Fabrication; Photonics; Silicon; Computer science; Materials science; Silicon photonics; Scanning electron microscope; Set (abstract data type); Degradation (telecommunications); Nanoscopic scale; Nanotechnology; Electronic engineering; Optoelectronics; Engineering; Telecommunications","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":[0.0006107751,0.0006623802,0.0004851857,0.000534074,0.0002625964,0.0005324368,0.001006891,0.0008536588,0.000663791],"category_scores_gemma":[0.003269004,0.0005253266,0.0003565345,0.0004227333,0.0005022134,0.000749535,0.0003537793,0.001090414,0.000174144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001324215,"about_ca_system_score_gemma":0.0008993894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008622128,"about_ca_topic_score_gemma":0.01583013,"domain_scores_codex":[0.999782,0.00003439839,0.00001145644,0.00008230587,0.00006186056,0.00002797039],"domain_scores_gemma":[0.9982576,0.00107154,0.0002025843,0.0001242631,0.0002894187,0.00005466985],"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":[0.00007217025,0.00006121283,0.00391187,0.00002680063,0.00001538728,0.0000453088,0.00001040454,0.9739748,0.003446471,0.0003886576,0.000552831,0.0174941],"study_design_scores_gemma":[0.000001573478,0.000004405988,0.0002748557,6.825303e-7,7.70897e-7,0.000003055209,0.00000105241,0.9983147,0.001126961,0.0002366164,0.0000338709,0.000001491833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7371168,0.0007577927,0.2555461,0.0008100503,0.00008648756,0.00007642509,0.001315713,0.002707393,0.001583365],"genre_scores_gemma":[0.9636157,0.0000907213,0.03462556,0.00007251075,0.00001951307,0.00004321408,0.0007957497,0.00005791281,0.0006791563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008622128,"threshold_uncertainty_score":0.01714391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197472665078164,"score_gpt":0.2298176774896952,"score_spread":0.2100704109818788,"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."}}