{"id":"W4387140984","doi":"10.1007/s00034-023-02509-w","title":"Low-Cost and Variation-Aware Spintronic Ternary Random Number Generator","year":2023,"lang":"en","type":"article","venue":"Circuits Systems and Signal Processing","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Ternary operation; Carbon nanotube field-effect transistor; Process variation; Transistor; Voltage; Fabrication; Generator (circuit theory); Random number generation; Stochastic process; Electrical engineering; Power (physics); Computer science; Topology (electrical circuits); Materials science; Electronic engineering; Mathematics; Field-effect transistor; Algorithm; Engineering; Physics; Statistics","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":[0.00009797778,0.0001984837,0.0002885872,0.0002463982,0.0002650832,0.0004741241,0.0007762667,0.0002933012,0.002161205],"category_scores_gemma":[0.0003666512,0.00009817763,0.0001256098,0.0002519687,0.0001668318,0.0006065808,0.0004091322,0.0003533399,0.0006564151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004566514,"about_ca_system_score_gemma":0.0002903161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002750616,"about_ca_topic_score_gemma":0.0009414887,"domain_scores_codex":[0.9998413,0.00002227735,0.000005883906,0.00002779579,0.00008230118,0.00002037206],"domain_scores_gemma":[0.9997966,0.0000385703,0.00004726308,0.00004287119,0.0000536913,0.0000210343],"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":[0.0003606979,0.0001708058,0.001709612,0.0002679265,0.00004286427,0.000428108,0.00007335175,0.01001265,0.8386035,0.04560962,0.009393964,0.09332683],"study_design_scores_gemma":[0.00009451158,0.0007253552,0.002890614,0.00004574621,0.00009840113,0.00185884,0.00005761487,0.37281,0.5783343,0.009494559,0.03349428,0.0000957158],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5775061,0.003139418,0.3434737,0.002718785,0.0009518563,0.0002255437,0.0008585047,0.004346209,0.06677993],"genre_scores_gemma":[0.9640075,0.0001612822,0.02940851,0.0001468481,0.00007109107,0.00002573903,0.0001706398,0.00007967974,0.005928684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002161205,"threshold_uncertainty_score":0.007229924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685668647939647,"score_gpt":0.244654963929894,"score_spread":0.2277982774504975,"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."}}