{"id":"W3151786832","doi":"10.1109/date.2010.5456941","title":"Practical Monte-Carlo based timing yield estimation of digital circuits","year":2010,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Variance reduction; Estimator; Monte Carlo method; Skew; Benchmark (surveying); Reduction (mathematics); Electronic circuit; Control variates; Importance sampling; Computer science; Algorithm; Statistics; Digital electronics; Sampling (signal processing); Mathematics; Engineering; Hybrid Monte Carlo; Detector","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":[0.001010646,0.0003885403,0.0004303603,0.0007872259,0.0002546259,0.0004993115,0.0005233889,0.0004036628,0.001633927],"category_scores_gemma":[0.004887776,0.0003029688,0.0002603371,0.0006098656,0.000365422,0.000590025,0.0004214332,0.0004308827,0.0003771215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006223067,"about_ca_system_score_gemma":0.0006836095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002055853,"about_ca_topic_score_gemma":0.002773774,"domain_scores_codex":[0.9994288,0.0001944521,0.00002202705,0.00005460282,0.0002723172,0.00002767247],"domain_scores_gemma":[0.998434,0.0010386,0.0001194472,0.0001679581,0.0002196206,0.00002034804],"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":[0.0001515273,0.00003657398,0.003549722,0.0001083892,0.00003915501,0.00007276719,0.00006210215,0.8407007,0.01737133,0.02509759,0.0006305663,0.1121794],"study_design_scores_gemma":[0.000003464789,0.00001676793,0.0004322714,0.000003439048,0.000003667316,0.00003703137,0.000003147639,0.992498,0.003754977,0.002780161,0.0004614012,0.000005635482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01733465,0.0001407567,0.9808558,0.00003266029,0.000005895143,0.00002223484,0.00004067013,0.0005324465,0.001034955],"genre_scores_gemma":[0.6246861,0.0003507727,0.3727419,0.00005266176,0.00002342957,0.000111401,0.0002618142,0.0001267535,0.001645093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002055853,"threshold_uncertainty_score":0.005466104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02822455049273304,"score_gpt":0.2494913202481173,"score_spread":0.2212667697553843,"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."}}