{"id":"W4399762235","doi":"10.1117/12.3016786","title":"Link loss analysis of integrated linear weight bank within silicon photonic neural network","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Neuromorphic engineering; Photonics; Electronic engineering; Radio frequency; Bandwidth (computing); Broadband; Computer science; Return loss; Silicon photonics; Insertion loss; Weighting; Amplifier; Artificial neural network; Electrical engineering; Engineering; Telecommunications; Materials science; Optoelectronics; Physics; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0004698882,0.000226645,0.0004390398,0.0003051416,0.0001051952,0.0002264703,0.00098658,0.0001073795,0.0001057309],"category_scores_gemma":[0.00001513408,0.0001520243,0.0003640186,0.005450963,0.00006837463,0.0002872673,0.0004290551,0.0004214234,0.00001984756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000318868,"about_ca_system_score_gemma":0.00008769707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001437098,"about_ca_topic_score_gemma":0.00009215881,"domain_scores_codex":[0.9979696,0.0001186239,0.0005536134,0.0006170507,0.0003075517,0.0004335871],"domain_scores_gemma":[0.9987607,0.0002678774,0.0001073749,0.00063211,0.0001104753,0.0001214849],"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":[0.00001113341,0.00002996176,0.002670648,0.00004130951,0.0008081548,0.0001518472,0.0002449626,0.9545084,0.0004870892,0.01603466,0.001534339,0.0234775],"study_design_scores_gemma":[0.00007953632,0.00007089003,0.0006219746,0.0000706822,0.0001381202,0.00001057776,0.000005827441,0.9946631,0.001266442,0.000414844,0.002470746,0.0001872523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6821542,0.003571274,0.3061105,0.00257715,0.003084019,0.0002578761,0.000005001118,0.0009141837,0.001325728],"genre_scores_gemma":[0.9870362,0.00005992711,0.01135117,0.0003379724,0.000467722,0.000003587649,0.00001393872,0.00001549013,0.0007140425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3048819,"threshold_uncertainty_score":0.6199369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01268198553863126,"score_gpt":0.2482346965773537,"score_spread":0.2355527110387224,"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."}}