{"id":"W1988461649","doi":"10.1109/icuwb.2015.7324405","title":"A Linearity Enhancement Method for CMOS Mixers Using Digital Assist","year":2015,"lang":"en","type":"article","venue":"","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"CMOS; Linearity; Frequency mixer; dBm; Electronic mixer; Electronic engineering; Electrical engineering; Radio frequency; Point (geometry); Power (physics); Harmonic mixer; Materials science; Computer science; Engineering; Physics; Mathematics; Amplifier","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":[0.0001824059,0.0003580584,0.0001518703,0.0003980563,0.0001880501,0.0003987888,0.0005030248,0.0002487278,0.001724939],"category_scores_gemma":[0.0004396761,0.0001764833,0.0001781841,0.000304471,0.0002401691,0.0004982347,0.0004095431,0.0004256101,0.0006197719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002230312,"about_ca_system_score_gemma":0.000143619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001412809,"about_ca_topic_score_gemma":0.0003478885,"domain_scores_codex":[0.9998678,0.00001612451,0.000008362859,0.00002833574,0.00006324329,0.00001605175],"domain_scores_gemma":[0.9997903,0.00006930973,0.00004145411,0.00004023907,0.00004962809,0.000009167376],"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":[0.0001263041,0.00002498134,0.0004165191,0.00007624697,0.00001059082,0.00007718004,0.00006057365,0.000677528,0.9085937,0.003256611,0.0003105538,0.08636925],"study_design_scores_gemma":[0.00002932312,0.0004707305,0.001033253,0.00001765289,0.00004885968,0.0009248566,0.00002694682,0.01439873,0.9613362,0.000948977,0.02074065,0.00002390477],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1507665,0.001865975,0.8340093,0.0003466317,0.0001721511,0.0001331311,0.0001076799,0.001886777,0.01071191],"genre_scores_gemma":[0.7130613,0.0008049366,0.2770535,0.0003114598,0.000166306,0.00006591009,0.0001207591,0.00006864862,0.008347146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001724939,"threshold_uncertainty_score":0.005770445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07960699201407757,"score_gpt":0.3098464663342719,"score_spread":0.2302394743201943,"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."}}