{"id":"W1595513435","doi":"10.1109/iccd.2004.1347903","title":"Functional illinois scan design at RTL","year":2004,"lang":"en","type":"article","venue":"","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Design for testing; Scan chain; Automatic test pattern generation; Test compression; Register-transfer level; Logic synthesis; Fault coverage; Logic gate; Test set; Compression (physics); Control logic; Test data; Set (abstract data type); Data flow diagram; Embedded system; Computer hardware; Reliability engineering; Algorithm; Engineering; Integrated circuit; Electronic circuit; Programming language; Database; Artificial intelligence; 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":[0.0003452481,0.000326289,0.000145943,0.0006131975,0.0002332537,0.0005988831,0.0005793198,0.000372195,0.008923217],"category_scores_gemma":[0.0007866566,0.0002173713,0.0002241743,0.000372583,0.0005555954,0.0008157946,0.0004166909,0.0005258514,0.00205499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005844488,"about_ca_system_score_gemma":0.0005168789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000766986,"about_ca_topic_score_gemma":0.00158053,"domain_scores_codex":[0.9994465,0.000140323,0.00002292152,0.00007271813,0.0002491098,0.00006853239],"domain_scores_gemma":[0.9995671,0.0001201787,0.00003468593,0.00009047558,0.0001716584,0.00001585075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005812648,0.00008055133,0.002501247,0.0004055139,0.0000444052,0.0006693287,0.0004977476,0.04753854,0.19593,0.357646,0.01387143,0.3802339],"study_design_scores_gemma":[0.000184939,0.001263775,0.001685752,0.0001503854,0.00007693994,0.002558163,0.0001252277,0.3097351,0.4030548,0.06458824,0.2164675,0.0001090373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0593476,0.0006397794,0.8091127,0.0006619895,0.0001183492,0.0001435402,0.0002665356,0.006508846,0.1232008],"genre_scores_gemma":[0.7477979,0.0002803684,0.2234713,0.00048049,0.00004775806,0.0001944229,0.0004630272,0.0004167641,0.02684794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008923217,"threshold_uncertainty_score":0.02985108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04926690952433731,"score_gpt":0.2176321969396519,"score_spread":0.1683652874153146,"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."}}