{"id":"W2773977158","doi":"10.1109/smc.2017.8122883","title":"Analysis of optimization algorithms in automated test pattern generation for sequential circuits","year":2017,"lang":"en","type":"article","venue":"","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"King Khalid University","keywords":"Automatic test pattern generation; Algorithm; Particle swarm optimization; Computer science; Differential evolution; Swarm intelligence; Multi-swarm optimization; Genetic algorithm; Fault coverage; Electronic circuit; Machine learning; Engineering","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":[0.002086295,0.000715752,0.0006413932,0.000890713,0.0003113915,0.0007284089,0.000488102,0.0007498315,0.0009960458],"category_scores_gemma":[0.009518084,0.0003946008,0.0005774956,0.0009125228,0.0006358975,0.0009714608,0.0002817478,0.0005919512,0.0001226186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012463,"about_ca_system_score_gemma":0.0009689633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002671265,"about_ca_topic_score_gemma":0.001383525,"domain_scores_codex":[0.9986703,0.0006514585,0.00007041508,0.0001226325,0.0003970161,0.00008822113],"domain_scores_gemma":[0.9933239,0.00567207,0.0003155154,0.0001800462,0.0004715934,0.00003682121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005253475,0.00004176382,0.001076428,0.00006965195,0.00003798731,0.0000298229,0.0000281466,0.9389032,0.001431049,0.01010045,0.0002546366,0.04797439],"study_design_scores_gemma":[0.000004662651,0.00002474288,0.0002938858,0.000005134362,0.000006681818,0.00001119661,0.000003910732,0.9966964,0.0005472315,0.002210212,0.0001936021,0.000002361447],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08446195,0.001477359,0.90824,0.0003831023,0.00002580492,0.0001001679,0.00003847912,0.0002605743,0.005012695],"genre_scores_gemma":[0.7997056,0.000955325,0.1971239,0.0001120161,0.00004620679,0.0002247934,0.0001355186,0.000126633,0.001570105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002671265,"threshold_uncertainty_score":0.01103354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0586881549721868,"score_gpt":0.3076898949084426,"score_spread":0.2490017399362558,"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."}}