{"id":"W2141524474","doi":"10.1080/00207543.2010.484429","title":"Optimisation of the process control in a semiconductor company: model and case study of defectivity sampling","year":2010,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Sampling (signal processing); Plan (archaeology); Control (management); Process (computing); Computer science; Metrology; Semiconductor device fabrication; Process control; Reliability engineering; Wafer fabrication; Manufacturing engineering; Set (abstract data type); Wafer; Industrial engineering; Engineering; Systems engineering; Artificial intelligence; Mathematics; Statistics","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":[0.0009326391,0.0006258267,0.0006455671,0.0004553281,0.0003916664,0.001118954,0.0009283601,0.00154086,0.001297643],"category_scores_gemma":[0.002131495,0.0003285238,0.0005938296,0.0004746083,0.0007059785,0.0004858727,0.0003952327,0.0007945729,0.0001320805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001180064,"about_ca_system_score_gemma":0.0008603119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01376376,"about_ca_topic_score_gemma":0.007492607,"domain_scores_codex":[0.9995585,0.000151992,0.00001749387,0.00007721137,0.0001014165,0.0000933652],"domain_scores_gemma":[0.9981878,0.001313651,0.0002012255,0.0000877813,0.0001535303,0.00005598428],"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":[0.0002189479,0.0001589338,0.002356511,0.0001048871,0.00002135669,0.0003906144,0.00009843171,0.9877543,0.002399801,0.001881269,0.0001063496,0.004508634],"study_design_scores_gemma":[0.00003601837,0.0002585285,0.001026272,0.000006347654,0.0000223161,0.00004385771,0.00005933483,0.9954776,0.002332875,0.0004895129,0.0002370202,0.0000104172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9325008,0.0004854061,0.05853719,0.0001946169,0.00001297692,0.000185336,0.0002063389,0.0001914899,0.007685754],"genre_scores_gemma":[0.9935794,0.0001175805,0.005034643,0.000006272796,0.000002397349,0.00005121101,0.00004522706,0.000005746051,0.001157573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01376376,"threshold_uncertainty_score":0.02736729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3725521970255248,"score_gpt":0.5762916478790965,"score_spread":0.2037394508535717,"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."}}