{"id":"W2900500071","doi":"10.1109/tvlsi.2018.2877438","title":"Efficient PUF-Based Key Generation in FPGAs Using Per-Device Configuration","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation","keywords":"Field-programmable gate array; Physical unclonable function; Computer science; Key generation; Embedded system; Overhead (engineering); Key (lock); Encryption; Virtex; Computer hardware; Cryptography; Advanced Encryption Standard; Algorithm; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007220739,0.0003794294,0.0003958129,0.0006014086,0.0007763235,0.0005115197,0.0004317058,0.0002474668,0.0001007465],"category_scores_gemma":[0.0000200768,0.0003619289,0.0002057471,0.00122416,0.0001001914,0.0007770014,0.000004765237,0.0004528102,0.0003484452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006132991,"about_ca_system_score_gemma":0.0002996972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006088456,"about_ca_topic_score_gemma":0.001903924,"domain_scores_codex":[0.9966606,0.0004681445,0.0007795852,0.0008376334,0.0007308893,0.0005231092],"domain_scores_gemma":[0.9980258,0.0001175953,0.0002260766,0.00077411,0.0006787828,0.0001776065],"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":[0.0001417702,0.002064961,0.00003213725,0.00008788358,0.00005934385,0.00001101959,0.003706003,0.8637492,0.1152052,0.008467351,0.0006344911,0.005840689],"study_design_scores_gemma":[0.0007806577,0.0002490271,0.00006664517,0.0001416515,0.00002512772,0.00001227563,0.0002753591,0.9278219,0.06841812,0.00004145424,0.001814229,0.0003535261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1782659,0.00003891306,0.8164396,0.0002189441,0.003455418,0.000645819,0.0000584458,0.0002411037,0.0006358874],"genre_scores_gemma":[0.9962329,0.000003961175,0.002466213,0.0002731929,0.0005389251,0.0001520307,0.0000437536,0.00002855913,0.0002604352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.817967,"threshold_uncertainty_score":0.9998833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02984465471118834,"score_gpt":0.2623921777810458,"score_spread":0.2325475230698575,"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."}}