{"id":"W2946222490","doi":"10.1109/jstqe.2019.2917501","title":"Silicon Photonic Circuit Design Using Rapid Prototyping Foundry Process Design Kits","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Quantum Electronics","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Applied Nanotools (Canada); Université Laval; Lumerical Solutions (Canada); University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photonics; Computer science; Lithography; Photonic integrated circuit; Rapid prototyping; Silicon photonics; Electronic circuit; Process (computing); Electronic engineering; Engineering; Materials science; Electrical engineering; Optoelectronics; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001504862,0.0003242962,0.0005853823,0.0004192798,0.0001647554,0.0002428115,0.001508322,0.0002131185,0.000007837137],"category_scores_gemma":[0.0001336824,0.0002908852,0.00013528,0.001856775,0.00003227862,0.0008234518,0.00007155137,0.00144407,0.000004527459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006260763,"about_ca_system_score_gemma":0.002118118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006239762,"about_ca_topic_score_gemma":0.000003803391,"domain_scores_codex":[0.9964128,0.0004894247,0.0009859339,0.0004506983,0.0006271846,0.001033993],"domain_scores_gemma":[0.9974739,0.0003557456,0.0008062038,0.0004107932,0.0008128904,0.0001404482],"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":[0.0002557595,0.0002919571,0.0008942738,0.0002386431,0.0001790923,0.0002124056,0.00102029,0.846994,0.1109468,0.00449179,0.00008847773,0.0343865],"study_design_scores_gemma":[0.0009103391,0.001117343,0.0001469623,0.0003647867,0.00001718607,0.0005615811,0.00001353736,0.9400813,0.04318964,0.01297594,0.0002679152,0.0003534561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5547238,0.002958124,0.4405686,0.0001234759,0.0007717194,0.0007767286,8.546073e-8,0.0000522543,0.00002519518],"genre_scores_gemma":[0.9866976,0.0006066292,0.0120116,0.0001765241,0.0004315943,0.00000949351,2.84556e-7,0.0000404674,0.00002578375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4319738,"threshold_uncertainty_score":0.9999543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0441963179957147,"score_gpt":0.2763656067241437,"score_spread":0.232169288728429,"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."}}