{"id":"W2057433396","doi":"10.1109/tvlsi.2014.2309439","title":"Functional Constraint Extraction From Register Transfer Level for ATPG","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Automatic test pattern generation; Computer science; VHDL; Code coverage; Register-transfer level; Process (computing); Reduction (mathematics); Functional verification; Hardware description language; Computer engineering; Scan chain; Design for testing; Fault coverage; Formal verification; Algorithm; Embedded system; Reliability engineering; Logic synthesis; Logic gate; Integrated circuit; Programming language; Engineering; Field-programmable gate array; Software; Testability; Electronic circuit","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":[0.0005184208,0.001000245,0.0004739695,0.001357601,0.0002974226,0.0006560899,0.0008682131,0.000746286,0.01009474],"category_scores_gemma":[0.003581841,0.000408776,0.0006769442,0.0009465209,0.0003370183,0.000740519,0.0005545727,0.0006225241,0.0016336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004514335,"about_ca_system_score_gemma":0.0009571138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00148086,"about_ca_topic_score_gemma":0.001800572,"domain_scores_codex":[0.9990485,0.0002642418,0.00008222792,0.0001008556,0.0004302313,0.0000739768],"domain_scores_gemma":[0.9979485,0.001253059,0.0001944767,0.0003319399,0.0002556517,0.00001637645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004921113,0.0001683258,0.002078753,0.001349882,0.00009401626,0.002029565,0.0003560414,0.2508118,0.1723007,0.03383549,0.01121595,0.5252673],"study_design_scores_gemma":[0.0001152845,0.000261286,0.0008965784,0.0001145516,0.00005075409,0.0008314979,0.00004944541,0.7411348,0.2142181,0.01600893,0.02625671,0.00006223784],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01908585,0.00009269349,0.9699625,0.00006778251,0.00002382005,0.0001746357,0.000702291,0.007024035,0.002866294],"genre_scores_gemma":[0.3262889,0.0002104995,0.6646469,0.0001456776,0.00002097438,0.0008396423,0.003022366,0.001656868,0.003168129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01009474,"threshold_uncertainty_score":0.03377026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05270942477915494,"score_gpt":0.2555873187919489,"score_spread":0.202877894012794,"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."}}