{"id":"W2804625592","doi":"10.1109/tvlsi.2018.2832472","title":"A Low-Power Pipelined-SAR ADC Using Boosted Bucket-Brigade Device for Residue Charge Processing","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Successive approximation ADC; CMOS; Dynamic range; Voltage; Computer science; Electronic engineering; Spurious-free dynamic range; Shaping; Low-power electronics; Figure of merit; Integral nonlinearity; Electrical engineering; Power (physics); Comparator; Physics; Engineering; Converters; Power consumption","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":[0.0002908025,0.0004596445,0.0006411377,0.0005035923,0.0003143609,0.0006015286,0.001571603,0.0006035175,0.004131982],"category_scores_gemma":[0.0004284797,0.0003529332,0.0003003631,0.0007014681,0.0002327911,0.001300368,0.0005279532,0.0007477839,0.001620459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003390757,"about_ca_system_score_gemma":0.0006435835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00042475,"about_ca_topic_score_gemma":0.001136899,"domain_scores_codex":[0.9997007,0.00002436411,0.00001998791,0.00006565844,0.0001474671,0.00004190982],"domain_scores_gemma":[0.9997787,0.00004287431,0.00003880222,0.00003957352,0.00007718649,0.0000228215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003145354,0.0001033247,0.0009379918,0.0004826467,0.00003777699,0.0004172165,0.00009339835,0.001161286,0.8594329,0.002665549,0.002625054,0.1317283],"study_design_scores_gemma":[0.0002497454,0.002314926,0.002811163,0.00009545746,0.0001510959,0.004740326,0.00007378801,0.05456408,0.8772812,0.001039652,0.05653574,0.0001426914],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2051914,0.004562175,0.7643785,0.0009530635,0.0006547187,0.0005766351,0.0008634444,0.007786145,0.0150339],"genre_scores_gemma":[0.645261,0.001224408,0.3428496,0.0005980019,0.0001615471,0.0001623153,0.0005715329,0.0001321466,0.009039425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004131982,"threshold_uncertainty_score":0.01382285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02276829116147401,"score_gpt":0.2542945131045413,"score_spread":0.2315262219430672,"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."}}