{"id":"W2151966987","doi":"10.1109/tvlsi.2009.2033697","title":"Analytical Soft Error Models Accounting for Die-to-Die and Within-Die Variations in Sub-Threshold SRAM Cells","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Static random-access memory; Soft error; CMOS; Die (integrated circuit); Electronic engineering; Threshold voltage; Monte Carlo method; Computer science; Electrical engineering; Voltage; Engineering; Transistor; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000708929,0.0004568105,0.0005554085,0.0007397452,0.0003120094,0.000287506,0.0002292956,0.00033806,0.00001169305],"category_scores_gemma":[0.00001234134,0.0004504858,0.0001624562,0.0004967181,0.00003581035,0.001178221,0.000002094189,0.0005446268,0.00005931674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003894287,"about_ca_system_score_gemma":0.00006632971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005619408,"about_ca_topic_score_gemma":0.0008367521,"domain_scores_codex":[0.9973637,0.00006039146,0.0009546086,0.0005535011,0.0004240879,0.0006436878],"domain_scores_gemma":[0.9988559,0.0001816235,0.0001002978,0.0004451227,0.000199881,0.0002171467],"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":[0.0000863945,0.0002207988,0.00002929418,0.0001146052,0.0000651978,0.000006066745,0.001881164,0.9862677,0.00887921,0.0003520826,0.0004485148,0.001649001],"study_design_scores_gemma":[0.0009133547,0.000179122,0.0001031529,0.0003058594,0.00008026337,0.000007850329,0.0004756502,0.9783355,0.01857689,0.0001723014,0.0003732914,0.0004767813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1777096,0.0001711618,0.8180519,0.0001136251,0.001806006,0.001230931,0.0002166136,0.0003936838,0.000306527],"genre_scores_gemma":[0.9970763,0.00005703324,0.001778439,0.0001231162,0.0001703798,0.0003673861,0.00002973085,0.00007853303,0.0003190574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8193668,"threshold_uncertainty_score":0.9997947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01689011626387884,"score_gpt":0.2356931316959709,"score_spread":0.2188030154320921,"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."}}