{"id":"W4283701272","doi":"10.1109/isqed54688.2022.9806266","title":"Multi-Objective Variation-Aware Sizing for Analog CNFET Circuits","year":2022,"lang":"en","type":"article","venue":"2022 23rd International Symposium on Quality Electronic Design (ISQED)","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Process variation; Carbon nanotube field-effect transistor; Computer science; Electronic circuit; Sizing; Robustness (evolution); Electronic engineering; Analogue electronics; Transistor; Process (computing); Field-effect transistor; Engineering; Electrical engineering; Voltage","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006208449,0.000926972,0.0004747334,0.0006281311,0.0002499991,0.0004769139,0.0006107434,0.0004963171,0.001122328],"category_scores_gemma":[0.0009967254,0.0002651797,0.0005755529,0.0003407315,0.0003129397,0.0004002602,0.0004171098,0.0004550024,0.000111793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007338914,"about_ca_system_score_gemma":0.0007923665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002375779,"about_ca_topic_score_gemma":0.002929474,"domain_scores_codex":[0.9997355,0.00006908644,0.000009406285,0.00004838112,0.0001062314,0.00003144067],"domain_scores_gemma":[0.9996315,0.0001987557,0.00005956356,0.00002034521,0.00007307045,0.00001678352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001294428,0.0000173017,0.0002627477,0.0000331155,0.00001235754,0.00002247986,0.00001722561,0.9743945,0.00596119,0.001096757,0.0001438222,0.01802551],"study_design_scores_gemma":[0.000004223969,0.00002823505,0.0001410179,0.000003156199,0.000005146851,0.000007699807,0.00000590937,0.997428,0.001436636,0.0006284693,0.0003087385,0.000002719755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07166909,0.0003013799,0.9234226,0.00009642782,0.00002685519,0.00008626669,0.0000481428,0.0002422071,0.004107045],"genre_scores_gemma":[0.7401672,0.0001564022,0.2569056,0.00007552293,0.00001345578,0.000207321,0.0001046913,0.00009907789,0.002270775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002375779,"threshold_uncertainty_score":0.005324841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03039699163710697,"score_gpt":0.289975327163967,"score_spread":0.25957833552686,"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."}}