{"id":"W2104162027","doi":"10.1109/iccad.1993.580118","title":"Cellular Automata Synthesis Based On Precomputed Test Vectors For Built-in Self-test","year":2005,"lang":"en","type":"article","venue":"Proceedings of 1993 International Conference on Computer Aided Design (ICCAD)","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Automatic test pattern generation; Cellular automaton; Test vector; Computer science; Fault coverage; Set (abstract data type); Built-in self-test; Test set; Automaton; Test (biology); Algorithm; Generator (circuit theory); Parallel computing; Simple (philosophy); Code coverage; Theoretical computer science; Embedded system; Programming language; Software; Artificial intelligence; Engineering; 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":[0.0001238064,0.0004760689,0.0003592231,0.0004068866,0.0001963185,0.0004118186,0.000656467,0.0003090344,0.001187633],"category_scores_gemma":[0.0007063925,0.0001787123,0.0003760332,0.0002703443,0.0003124478,0.0003277983,0.00026749,0.0004149082,0.0003526512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004725462,"about_ca_system_score_gemma":0.0007479407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001664569,"about_ca_topic_score_gemma":0.002374793,"domain_scores_codex":[0.9997852,0.00004061461,0.00001850842,0.00004966534,0.00007707908,0.00002884889],"domain_scores_gemma":[0.9996074,0.000163071,0.0000431729,0.00008279049,0.00008672571,0.00001690332],"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":[0.0002057972,0.000158797,0.001399023,0.000307114,0.00008490912,0.0005083904,0.0001433515,0.3754047,0.234287,0.1043748,0.002537318,0.2805888],"study_design_scores_gemma":[0.00005545817,0.0002182168,0.0002862857,0.00002114546,0.00003952675,0.0001703087,0.00001072537,0.9070469,0.07503121,0.01134918,0.005747459,0.00002359345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04094285,0.0002052331,0.9523159,0.00007745994,0.00009267568,0.0001088178,0.0001392513,0.002143674,0.003974064],"genre_scores_gemma":[0.6061605,0.0001825413,0.3905317,0.00007029733,0.00002445361,0.0003204412,0.0003605723,0.00008651949,0.002263003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001664569,"threshold_uncertainty_score":0.003973007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04833540597834448,"score_gpt":0.2653379253201593,"score_spread":0.2170025193418148,"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."}}