{"id":"W4391410273","doi":"10.1109/mwscas57524.2023.10405927","title":"A Cyclic Vernier Digital-to-Time Converter for Time-Mode Successive Approximation TDC","year":2023,"lang":"en","type":"article","venue":"","topic":"Advancements in PLL and VCO Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Toronto Metropolitan University","funders":"","keywords":"Vernier scale; Converters; Dynamic range; Time-to-digital converter; Integral nonlinearity; Least significant bit; Differential nonlinearity; Successive approximation ADC; Computer science; Wide dynamic range; Electronic engineering; Calibration; Range (aeronautics); Voltage; Physics; Electrical engineering; Materials science; Engineering; Optics; Capacitor","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":[0.0003266603,0.0002897291,0.0002130378,0.0005392868,0.0002698594,0.0005899382,0.0006890108,0.0004388807,0.004614883],"category_scores_gemma":[0.0008780938,0.0001323183,0.0001976641,0.0004752654,0.0002333658,0.0005395385,0.0002169282,0.0004786877,0.0007729892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004350525,"about_ca_system_score_gemma":0.0004329444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006188228,"about_ca_topic_score_gemma":0.001266738,"domain_scores_codex":[0.9996361,0.00005016926,0.00002304363,0.00006715258,0.0001925436,0.00003101657],"domain_scores_gemma":[0.9996378,0.0001090832,0.00004140276,0.00007540036,0.000114103,0.00002220895],"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":[0.0003877127,0.00008099333,0.001287165,0.0003282391,0.00004508903,0.0003993835,0.0001857896,0.01105242,0.5394584,0.03113084,0.00436755,0.4112764],"study_design_scores_gemma":[0.00008026349,0.001180167,0.001732845,0.00008935988,0.0001076501,0.003705224,0.00005857849,0.3081955,0.5672709,0.003487052,0.1139725,0.000119961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1060868,0.002902047,0.8500026,0.0004769721,0.0006450279,0.0002470587,0.0003291068,0.004511385,0.03479889],"genre_scores_gemma":[0.7207903,0.0006967804,0.2692104,0.000375579,0.0001661131,0.0000898514,0.0002641061,0.0001586373,0.00824824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004614883,"threshold_uncertainty_score":0.01543832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008911150256715808,"score_gpt":0.2512234302327795,"score_spread":0.2423122799760637,"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."}}