{"id":"W3158881598","doi":"10.1109/iscas51556.2021.9401697","title":"A Scalable Many-Stage CMOS OTA for Closed-Loop Applications","year":2021,"lang":"en","type":"article","venue":"","topic":"Integrated Circuits and Semiconductor Failure Analysis","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Scalability; CMOS; Stage (stratigraphy); Electronic engineering; Computer science; Loop gain; Loop (graph theory); Compensation (psychology); Stability (learning theory); Frequency compensation; Single stage; Engineering; Electrical engineering; Voltage; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005351974,0.0001156835,0.0001617282,0.00005486763,0.00007166984,0.00006625315,0.0001123212,0.00008118878,0.001626381],"category_scores_gemma":[0.00001057541,0.000106767,0.0001245232,0.0003323516,0.00001127825,0.00008124652,0.000009880091,0.00009770969,0.0001554171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004058188,"about_ca_system_score_gemma":0.00002987673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003124631,"about_ca_topic_score_gemma":0.0001060564,"domain_scores_codex":[0.9993259,0.000006143148,0.0001785557,0.000187687,0.00007683389,0.0002248276],"domain_scores_gemma":[0.9994372,0.00003479954,0.00001346761,0.0003106188,0.0001407575,0.00006314476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003841393,0.0001377713,0.0003159665,0.0003100541,0.0008497917,0.00001546817,0.0002211525,0.007419662,0.5872721,0.2544969,0.1168046,0.03215266],"study_design_scores_gemma":[0.0002623485,0.000009303116,0.00002285606,0.000009500976,0.0001068091,0.000005275983,0.0005580032,0.08412737,0.1560751,0.0007232446,0.757821,0.0002792114],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04119974,0.001465055,0.7398338,0.0005902264,0.0002920032,0.0007479003,0.0001800635,0.001016364,0.2146749],"genre_scores_gemma":[0.8429272,0.0001451381,0.00576555,0.0004898175,0.0002635598,0.0005740399,0.0003412038,0.0000830813,0.1494104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8017274,"threshold_uncertainty_score":0.9992863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01389442036862006,"score_gpt":0.2289438154197301,"score_spread":0.21504939505111,"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."}}