{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003757829,0.0002835963,0.0001791941,0.0003098897,0.0002349841,0.0003691269,0.0006335537,0.0003727242,0.001325243],"category_scores_gemma":[0.0006279941,0.0001397362,0.0001591888,0.0003339607,0.0003166563,0.0006994805,0.0002425494,0.0002909215,0.0002335419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007634523,"about_ca_system_score_gemma":0.0001871334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001702261,"about_ca_topic_score_gemma":0.001807816,"domain_scores_codex":[0.9997249,0.00003983204,0.000007269362,0.00004491816,0.000145439,0.00003762298],"domain_scores_gemma":[0.9995987,0.0001558986,0.00007418226,0.00003122111,0.0001264775,0.00001349006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001446394,0.0004959573,0.01042616,0.0002273601,0.0001564795,0.0006659587,0.0002543356,0.400203,0.5227268,0.005688074,0.001655029,0.05605451],"study_design_scores_gemma":[0.00001038528,0.0004178172,0.002834501,0.000008113915,0.00002744387,0.00008208954,0.00004655878,0.8649127,0.1306109,0.0004359859,0.000599174,0.00001433621],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9468181,0.0002243095,0.04810487,0.0001343466,0.00002228012,0.00001684022,0.00009369301,0.0003617923,0.00422386],"genre_scores_gemma":[0.9954277,0.0000597866,0.003248764,0.00002180926,0.000002311785,0.00000843449,0.0000433235,0.00001614316,0.001171653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001702261,"threshold_uncertainty_score":0.005539238,"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."}}