{"id":"W2048703883","doi":"10.1117/12.861038","title":"Dual-illumination NIR system for wafer level defect inspection","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Semiconductor Detectors and Materials","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Redlen Technologies (Canada)","funders":"U.S. Department of Homeland Security","keywords":"Wafer; Detector; Computer science; Materials science; Fabrication; Process (computing); Automated X-ray inspection; Stack (abstract data type); Focus (optics); Optics; Optoelectronics; Image processing; Artificial intelligence; Telecommunications","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":[0.0005647708,0.000470633,0.0004806641,0.000805276,0.0003517924,0.0004662612,0.0009823532,0.0008805766,0.007541761],"category_scores_gemma":[0.0005975068,0.0003606968,0.0003194273,0.0003426666,0.0002337869,0.0006827,0.0009954163,0.0007854997,0.002657134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005055373,"about_ca_system_score_gemma":0.0004722697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004736701,"about_ca_topic_score_gemma":0.001054802,"domain_scores_codex":[0.9993665,0.00009273546,0.00002030438,0.0001625818,0.0003093692,0.00004855751],"domain_scores_gemma":[0.9994648,0.0001267835,0.00007363463,0.0001361294,0.0001487787,0.00004987988],"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":[0.0002642836,0.000113111,0.001350054,0.0001468835,0.00001873922,0.00008630559,0.00006750626,0.0006026081,0.9390516,0.0009746731,0.004111181,0.05321305],"study_design_scores_gemma":[0.0001094741,0.0005608574,0.009253799,0.00004004891,0.00006024577,0.001287567,0.00004879326,0.07104769,0.8882496,0.0005357254,0.02867513,0.0001310466],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2464396,0.0009478278,0.7044228,0.0004323948,0.000314423,0.0005850379,0.00198319,0.02471167,0.02016304],"genre_scores_gemma":[0.4333487,0.000481092,0.5494351,0.0003769929,0.0001069919,0.000777641,0.001129833,0.0005577612,0.01378584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007541761,"threshold_uncertainty_score":0.02522963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01353698488983107,"score_gpt":0.2256126305888468,"score_spread":0.2120756456990157,"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."}}