{"id":"W4409329969","doi":"10.1109/tcsi.2025.3556802","title":"A Multi-Stage RC Compensation Technique for Decoupling the Transimpedance and BW: Creating High Speed and Low Noise TIA Designs","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Integrated Circuits and Semiconductor Failure Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decoupling (probability); Transimpedance amplifier; Electronic engineering; Noise (video); Compensation (psychology); Computer science; Stage (stratigraphy); RC circuit; Operational amplifier; Electrical engineering; Engineering; Capacitor; Voltage; Amplifier; CMOS; Control engineering; Artificial intelligence; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003760825,0.0002558368,0.0003540871,0.000180526,0.0004769332,0.000174233,0.00008448857,0.000176032,0.000007802817],"category_scores_gemma":[0.000009054447,0.000202265,0.00008564711,0.000235309,0.00009037316,0.0001180252,6.196248e-7,0.0002249968,4.070217e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006334013,"about_ca_system_score_gemma":0.00003228171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001956953,"about_ca_topic_score_gemma":0.0001885579,"domain_scores_codex":[0.9989038,0.0000588357,0.0003508725,0.000328219,0.00011481,0.0002434669],"domain_scores_gemma":[0.9993353,0.0002262138,0.00005231452,0.0002203173,0.00008076348,0.00008509113],"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":[0.000009648352,0.00002328548,0.00003085898,0.0005116056,0.0003805264,0.000002380451,0.001013759,0.07131322,0.9183788,0.0006261998,0.00001302197,0.007696715],"study_design_scores_gemma":[0.002029446,0.0001311673,0.0002925832,0.001005467,0.0008445745,0.00005368729,0.004973206,0.8348906,0.1539107,0.00005331126,0.001059229,0.0007559924],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.199511,0.000839102,0.7976485,0.00006263226,0.0002734361,0.001108782,0.00007454246,0.0001317817,0.00035022],"genre_scores_gemma":[0.9986559,0.0003121865,0.0002240637,0.00004753209,0.00002662119,0.0001489911,0.000006263546,0.00003550008,0.0005429457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7991449,"threshold_uncertainty_score":0.8248126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02482906499420183,"score_gpt":0.2443884566543809,"score_spread":0.219559391660179,"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."}}