{"id":"W4388000438","doi":"10.23977/jeis.2023.080409","title":"Uniformity optimization method for arbiter physically unclonable functions","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Arbiter; Physical unclonable function; Reliability (semiconductor); Uniqueness; Scheme (mathematics); Computer science; Reliability engineering; Encryption; Algorithm; Embedded system; Cryptography; Hardware security module; Computer engineering; Field-programmable gate array; Computer hardware; Mathematics; Engineering; Computer security; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00177052,0.00009277122,0.0001542939,0.0004851746,0.0005260107,0.0004397537,0.0004868267,0.00003502148,0.000004077109],"category_scores_gemma":[0.0002649088,0.00007428298,0.00008212568,0.00227804,0.00007385815,0.01184258,0.0001197234,0.0001810762,0.00001204139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001087787,"about_ca_system_score_gemma":0.0006682985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002589429,"about_ca_topic_score_gemma":8.882325e-7,"domain_scores_codex":[0.9986296,0.0000201553,0.0003938975,0.0001261014,0.0004907062,0.0003395352],"domain_scores_gemma":[0.998105,0.0001336143,0.0003318083,0.0001870186,0.001107296,0.0001352279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004103192,0.00007367835,0.0000401716,0.00003480603,0.00002170848,5.372062e-7,0.0006935904,0.1226731,0.001572864,0.8202218,0.003834586,0.05079211],"study_design_scores_gemma":[0.0003595484,0.0003733597,0.0004843992,0.000009490754,0.00001021569,0.00002530048,0.00005405328,0.9057049,0.0008264464,0.01731255,0.07473303,0.0001067553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003550893,0.00002646818,0.9932233,0.001429344,0.0003004217,0.0001249129,0.000005377227,0.00004654662,0.001292711],"genre_scores_gemma":[0.5000256,0.0009713256,0.4950838,0.002790031,0.0005064428,0.00004700397,0.00003856046,0.00001685049,0.0005204277],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8029093,"threshold_uncertainty_score":0.8585584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00907270163719646,"score_gpt":0.2668169760154485,"score_spread":0.257744274378252,"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."}}