{"id":"W2143528444","doi":"10.5555/1129601.1129660","title":"Noise margin analysis for dynamic logic circuits","year":2005,"lang":"en","type":"article","venue":"International Conference on Computer Aided Design","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Domino logic; Noise margin; Electronic circuit; Computer science; Robustness (evolution); Noise (video); Electronic engineering; Logic gate; Waveform; Logic optimization; Noise immunity; Logic synthesis; Control theory (sociology); Logic family; Algorithm; Engineering; Transistor; Electrical engineering; Artificial intelligence; Voltage; Telecommunications","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":[0.0008447708,0.0007482094,0.0003793983,0.0007679228,0.0002889498,0.0007696053,0.0005224314,0.0005500186,0.002223147],"category_scores_gemma":[0.004450781,0.0002261105,0.0003196004,0.0003925478,0.0006921089,0.0009541056,0.0005985313,0.0005349829,0.0002601991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009751776,"about_ca_system_score_gemma":0.0003880766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008053342,"about_ca_topic_score_gemma":0.0005177418,"domain_scores_codex":[0.9994394,0.0001320476,0.0000153866,0.00008596105,0.0002794059,0.00004781078],"domain_scores_gemma":[0.9988616,0.0007796549,0.0001103972,0.00003923687,0.0001802576,0.00002894225],"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":[0.0001388945,0.00003755331,0.0004931196,0.0001350321,0.0000461423,0.0001206678,0.00009990234,0.8189235,0.03983288,0.09008601,0.0009203848,0.04916592],"study_design_scores_gemma":[0.000005518432,0.00004417983,0.0001318071,0.000009647234,0.00001041368,0.00002920361,0.00001000592,0.9587194,0.005582324,0.03459666,0.0008534889,0.000007431543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02083934,0.0003341172,0.9743913,0.0001887846,0.00001545209,0.0000245298,0.00003879765,0.0001455132,0.00402214],"genre_scores_gemma":[0.9315146,0.0005614067,0.0630825,0.0001424441,0.00008217709,0.0001023653,0.0001186042,0.0001021984,0.004293547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002223147,"threshold_uncertainty_score":0.00743711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04891731290731074,"score_gpt":0.2742954775781714,"score_spread":0.2253781646708606,"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."}}