{"id":"W4387252994","doi":"10.1063/5.0159928","title":"Reinforcement learning for photonic component design","year":2023,"lang":"en","type":"article","venue":"APL Photonics","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Reinforcement learning; Photonics; Grating; Bandwidth (computing); Photonic crystal; Computer science; Materials science; Silicon on insulator; Nanolithography; Optoelectronics; Silicon; Electronic engineering; Fabrication; Engineering; Telecommunications; Artificial intelligence","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":[0.001089598,0.0006299327,0.0006043816,0.0002847505,0.0002567801,0.0005322681,0.000780092,0.0006846652,0.001929043],"category_scores_gemma":[0.002366976,0.0002795154,0.0003441253,0.000286454,0.0007839227,0.0004572137,0.0005923937,0.001143241,0.000332727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009115956,"about_ca_system_score_gemma":0.0008838422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002162305,"about_ca_topic_score_gemma":0.00185805,"domain_scores_codex":[0.9996572,0.0001232673,0.00001409752,0.00005288828,0.000112345,0.00004020946],"domain_scores_gemma":[0.9992199,0.0005258515,0.00006007552,0.0000436825,0.0001212518,0.00002926038],"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":[0.00002964088,0.00003622896,0.0002588455,0.00004566107,0.00002085118,0.00002374427,0.00002082777,0.9355751,0.001255917,0.01512917,0.0006012465,0.0470027],"study_design_scores_gemma":[0.000006742636,0.00001697847,0.00001775176,0.000003612627,0.000002038187,0.000004320611,0.00000146688,0.9945069,0.00033104,0.00463008,0.0004769391,0.000002147798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004857185,0.000300356,0.9925352,0.0001264841,0.00002988053,0.0000265654,0.000007245143,0.0001805453,0.001936485],"genre_scores_gemma":[0.6011454,0.0005918336,0.3926914,0.0002265011,0.00008730361,0.000319449,0.00006620152,0.00008830072,0.004783743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002162305,"threshold_uncertainty_score":0.006614089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02844729145999219,"score_gpt":0.2449575574477484,"score_spread":0.2165102659877562,"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."}}