{"id":"W1568327405","doi":"10.1109/iscas.2006.1692765","title":"Digital Background Calibration of Interstage-Gain and Capacitor-Mismatch Errors in Pipelined ADCs","year":2006,"lang":"en","type":"article","venue":"","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Capacitor; Spurious-free dynamic range; Operational amplifier; Pipeline (software); Calibration; Electronic engineering; Converters; Linearity; Computer science; Switched capacitor; Amplifier; Electrical engineering; Engineering; Voltage; CMOS; 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":[],"consensus_categories":[],"category_scores_codex":[0.00007452065,0.0001042768,0.0001454398,0.00009258593,0.00001159194,0.00003321117,0.00004906662,0.00006693772,0.00003345863],"category_scores_gemma":[0.000006213534,0.000100023,0.00002719428,0.000114695,0.00003753772,0.0002695828,0.000007376834,0.0000747884,0.000004156256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003400876,"about_ca_system_score_gemma":0.000008168089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002276402,"about_ca_topic_score_gemma":0.000229481,"domain_scores_codex":[0.9994178,0.0000122789,0.0002473982,0.0001037787,0.00008130092,0.0001374683],"domain_scores_gemma":[0.9998159,0.00003748444,0.00002052927,0.00007845804,0.00001421318,0.00003337895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001872208,0.0006054216,0.05589519,0.001569196,0.000255473,0.0001906116,0.005363276,0.1355597,0.387995,0.2817199,0.07325073,0.05740826],"study_design_scores_gemma":[0.005722817,0.0005259393,0.01282351,0.0003920084,0.00008921607,0.00007949625,0.0154179,0.8093042,0.1065778,0.04175097,0.004761257,0.002554844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4414158,0.0001320892,0.5404993,0.000006622352,0.0000643638,0.00008603668,0.00001229347,0.00009027304,0.0176933],"genre_scores_gemma":[0.9987683,0.000006146349,0.00007292091,0.00001216794,0.00004759402,0.000003110838,0.00002920323,0.00001630449,0.001044276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6737446,"threshold_uncertainty_score":0.4078822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01140655961242413,"score_gpt":0.1951791883550611,"score_spread":0.183772628742637,"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."}}