{"id":"W4379538201","doi":"10.1021/acsphotonics.3c00389","title":"Improving Fabrication Fidelity of Integrated Nanophotonic Devices Using Deep Learning","year":2023,"lang":"en","type":"article","venue":"ACS Photonics","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"National Research Council Canada","keywords":"Computer science; Fabrication; Miniaturization; Nanophotonics; Design space exploration; Photonics; Leverage (statistics); Deep learning; Optical proximity correction; Process (computing); Computer architecture; Artificial intelligence; Materials science; Nanotechnology; Embedded system; Optoelectronics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001015935,0.0006695722,0.0003850956,0.0003500222,0.0003105323,0.0009419381,0.001234481,0.0007031005,0.002305979],"category_scores_gemma":[0.003202092,0.0005916894,0.0004204694,0.0002747264,0.000624104,0.001400996,0.001057539,0.001463631,0.0007199617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001931738,"about_ca_system_score_gemma":0.001294202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002438747,"about_ca_topic_score_gemma":0.006080533,"domain_scores_codex":[0.9994804,0.00003915477,0.0000282716,0.0001240449,0.0002775736,0.00005054653],"domain_scores_gemma":[0.9989315,0.0003646969,0.0001616864,0.0003401429,0.0001670152,0.00003486997],"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":[0.00016579,0.0002303951,0.002838872,0.0003143404,0.00007854074,0.0001883385,0.0001077066,0.6282098,0.2224019,0.01159959,0.00319152,0.1306733],"study_design_scores_gemma":[0.00001060086,0.00005673109,0.0004069505,0.00001463722,0.000008565244,0.00004249092,0.00001119193,0.9159337,0.07912674,0.002298635,0.002074849,0.00001490167],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1238244,0.0003855292,0.8617976,0.0004820195,0.0001057035,0.00009177654,0.0004148764,0.005982664,0.006915544],"genre_scores_gemma":[0.6296434,0.0002706063,0.366172,0.0001844634,0.00001393903,0.0001108169,0.0005431699,0.000493282,0.002568271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002438747,"threshold_uncertainty_score":0.01401585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468189088847482,"score_gpt":0.2414442678227867,"score_spread":0.2267623769343119,"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."}}