{"id":"W2291446711","doi":"10.1117/12.2213549","title":"Toward designing back-illuminated CMOS image sensor based on 3D modeling","year":2016,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Crosslight Software (Canada)","funders":"","keywords":"Image sensor; Pixel; CMOS; Finite-difference time-domain method; Sensitivity (control systems); CMOS sensor; Wavelength; Electronic engineering; Computer science; Optics; Optoelectronics; Materials science; Artificial intelligence; Engineering; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005096025,0.0004832183,0.0004906091,0.0001739597,0.00007928039,0.0001238949,0.0007920082,0.0002174154,0.0000388274],"category_scores_gemma":[0.0005081191,0.0003627969,0.000614407,0.000339888,0.0001981515,0.0005568356,0.00008376231,0.0003400139,0.00001423844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002757224,"about_ca_system_score_gemma":0.00002670169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005627472,"about_ca_topic_score_gemma":3.876685e-8,"domain_scores_codex":[0.9974513,2.43715e-8,0.0007377624,0.0004605983,0.0007323714,0.000617917],"domain_scores_gemma":[0.9981545,0.0002232731,0.0001817947,0.0000933389,0.00117564,0.0001715106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009729288,0.00007994235,0.0001036593,0.000600184,0.0002961572,4.44827e-7,0.0001733708,0.01311918,0.9598655,0.022515,0.00241477,0.0007345284],"study_design_scores_gemma":[0.001224055,0.0001504959,0.00005327264,0.0007309291,0.0000933977,0.00001005927,0.0004902868,0.7493975,0.2462724,0.0002534971,0.0008585618,0.0004655858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894339,0.00004283748,0.003108703,0.001845434,0.0003141722,0.0004648587,0.00006035277,0.0003545055,0.004375258],"genre_scores_gemma":[0.7692109,0.00006364001,0.2298124,0.0001329106,0.0003297856,0.00008772919,0.000005966262,0.0001636911,0.000192938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7362783,"threshold_uncertainty_score":0.9998824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01437433547194918,"score_gpt":0.2160676181004764,"score_spread":0.2016932826285273,"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."}}