{"id":"W2087825164","doi":"10.1016/j.vlsi.2005.08.002","title":"Automatic generation of defect injectable VHDL fault models for ASIC standard cell libraries","year":2005,"lang":"en","type":"article","venue":"Integration","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Application-specific integrated circuit; VHDL; Computer science; Fault injection; Embedded system; Software; Fault (geology); Task (project management); Computer architecture; Standard cell; Testability; Reliability engineering; Integrated circuit; Computer engineering; Engineering; Field-programmable gate array; Systems engineering; Programming language; Operating system","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":[0.0003473539,0.0008935592,0.0003951033,0.0009917505,0.0002444716,0.0007620255,0.001330604,0.000566394,0.002447886],"category_scores_gemma":[0.001561123,0.0004786067,0.0005673097,0.0003897891,0.0002944497,0.0007570848,0.0003220374,0.0005160733,0.000513082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001084774,"about_ca_system_score_gemma":0.0009878573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002569563,"about_ca_topic_score_gemma":0.005350598,"domain_scores_codex":[0.9996387,0.00006857898,0.00001974817,0.00004989075,0.0001802124,0.00004284141],"domain_scores_gemma":[0.9989129,0.0004392706,0.0002015893,0.0001695577,0.0002525463,0.00002408133],"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":[0.0003165106,0.0001730415,0.002290931,0.0003190052,0.00008414424,0.000343249,0.0002002918,0.8041177,0.07596642,0.0117859,0.00374334,0.1006594],"study_design_scores_gemma":[0.00002846575,0.00007439894,0.0001470258,0.00001110355,0.0000304991,0.0000681235,0.00001442824,0.9418436,0.05396057,0.002204395,0.001608047,0.000009335522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1224352,0.0001501629,0.8599548,0.0001301206,0.00004030964,0.0001442153,0.001186732,0.01268092,0.003277545],"genre_scores_gemma":[0.8260858,0.0001536625,0.1688816,0.00008421474,0.00001405434,0.0001910928,0.001659256,0.0008340632,0.002096139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002569563,"threshold_uncertainty_score":0.008188963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04153739921647402,"score_gpt":0.2508200225653761,"score_spread":0.2092826233489021,"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."}}