{"id":"W4387717672","doi":"10.1109/tmtt.2023.3323042","title":"A 0.1–20.1-GHz Wideband Noise-Canceling g<sub>m</sub>-Boosted CMOS LNA With Gain-Reuse","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Wideband; Low-noise amplifier; CMOS; Notation; Amplifier; Mathematics; Algorithm; Electronic engineering; Computer science; Electrical engineering; Topology (electrical circuits); Engineering; Arithmetic","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":[0.000162361,0.0004208557,0.0003121622,0.00029879,0.0002758584,0.0006339475,0.0009589475,0.0007031551,0.001875345],"category_scores_gemma":[0.0002438482,0.0002098131,0.0004469024,0.0003081535,0.0002907268,0.0007193119,0.0003976791,0.000493963,0.002363021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005070768,"about_ca_system_score_gemma":0.0003977155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006724076,"about_ca_topic_score_gemma":0.001357245,"domain_scores_codex":[0.9998338,0.0000195871,0.000008280185,0.00006265134,0.00005789121,0.00001770212],"domain_scores_gemma":[0.9999022,0.00001414729,0.00001880062,0.00001587625,0.00003666107,0.00001216594],"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":[0.0001670489,0.00003977879,0.0005074093,0.00009740939,0.00003303837,0.0003036585,0.00009459799,0.003486268,0.9398223,0.007453823,0.001017818,0.04697683],"study_design_scores_gemma":[0.00006488201,0.0007242002,0.001539901,0.00005421415,0.0002022205,0.003026207,0.00005968473,0.08656975,0.8229043,0.003424699,0.08135021,0.00007981119],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.151142,0.001944753,0.8127254,0.0009804838,0.0004802351,0.0001231182,0.0002474788,0.004561577,0.027795],"genre_scores_gemma":[0.7186338,0.001053264,0.2499742,0.0006439718,0.0002640824,0.00008577901,0.0002821715,0.0002182603,0.02884432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001875345,"threshold_uncertainty_score":0.006273627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0105263693344486,"score_gpt":0.20929546150623,"score_spread":0.1987690921717813,"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."}}