{"id":"W1976619200","doi":"10.1007/s10470-011-9804-5","title":"A capacitor scaling and capacitor sharing technique for reducing power dissipation in algorithmic ADCs","year":2011,"lang":"en","type":"article","venue":"Analog Integrated Circuits and Signal Processing","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Capacitor; Dissipation; Scaling; Electronic engineering; Decoupling capacitor; Power (physics); Electrical engineering; Computer science; Materials science; Engineering; Physics; Mathematics; Voltage","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":[0.0005147679,0.0003442627,0.0003930141,0.0003917261,0.000232763,0.0001435087,0.0001475752,0.0002607063,0.00002509834],"category_scores_gemma":[0.00004378893,0.0003223555,0.00005917086,0.0004034635,0.0001147213,0.0005076649,0.00001525935,0.0004338633,0.000001210195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001083249,"about_ca_system_score_gemma":0.00006105841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002701944,"about_ca_topic_score_gemma":0.00003263097,"domain_scores_codex":[0.9983308,0.00003488985,0.0004988757,0.0004939295,0.0001418895,0.0004995731],"domain_scores_gemma":[0.9994161,0.00005851,0.00009427026,0.0001217484,0.0001455854,0.0001637617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005403499,0.000114539,0.003315642,0.001097403,0.0001404885,0.00006841854,0.01378998,0.0004221963,0.4581643,0.005269934,0.0001192544,0.5174438],"study_design_scores_gemma":[0.004465297,0.001301456,0.009789984,0.009924054,0.0005449812,0.0006801491,0.01619205,0.6439402,0.2332788,0.07413672,0.0006137962,0.005132511],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1398834,0.002110018,0.8557764,0.000002933912,0.0001016063,0.0005202882,0.00002257463,0.0002014225,0.001381333],"genre_scores_gemma":[0.9988368,0.00006087802,0.0006636843,0.00003155875,0.0001072497,0.0001742883,0.00002248064,0.00006841201,0.00003465428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8589534,"threshold_uncertainty_score":0.9999229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02473563339357963,"score_gpt":0.2234613662614343,"score_spread":0.1987257328678547,"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."}}