{"id":"W2997436302","doi":"10.1364/oe.381921","title":"Efficient layout-aware statistical analysis for photonic integrated circuits","year":2020,"lang":"en","type":"article","venue":"Optics Express","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Computer science; Electronic engineering; Filter (signal processing); Integrated circuit; Cholesky decomposition; Computation; Algorithm; Engineering; Mathematics; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004612153,0.0003961939,0.0003357756,0.000704349,0.0003486395,0.0004501907,0.0006511789,0.0004335842,0.001059968],"category_scores_gemma":[0.001985857,0.0003917128,0.0005022541,0.0005758333,0.0003392251,0.0005119484,0.0003272422,0.0005103567,0.0002342247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037183,"about_ca_system_score_gemma":0.001545834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005976972,"about_ca_topic_score_gemma":0.007263881,"domain_scores_codex":[0.9997062,0.00007483041,0.000008914778,0.00002571918,0.0001568829,0.00002745743],"domain_scores_gemma":[0.9991679,0.0004643505,0.00009317221,0.0001169384,0.0001385413,0.00001914498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009274788,0.00001291005,0.0004878647,0.0000202357,0.00001862771,0.00002455774,0.000008469995,0.9810037,0.00352734,0.006507463,0.0002557936,0.008123627],"study_design_scores_gemma":[7.610964e-7,0.000002783348,0.0000663411,8.071421e-7,0.0000012448,0.000003720794,9.268679e-7,0.9984391,0.0005333864,0.0008277022,0.0001217869,0.000001289167],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03209335,0.0002389617,0.964374,0.0001168476,0.00001579368,0.00004563484,0.0001545001,0.0009713712,0.001989587],"genre_scores_gemma":[0.7135807,0.0004220202,0.2836419,0.0001130808,0.00004409726,0.0001997482,0.0004911297,0.0002789282,0.001228366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005976972,"threshold_uncertainty_score":0.01188439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02012787312910766,"score_gpt":0.2437848842548642,"score_spread":0.2236570111257566,"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."}}