{"id":"W4200015366","doi":"10.36227/techrxiv.16967275.v1","title":"ViTaL: Verifying Trojan-Free Physical Layouts through Hardware Reverse Engineering","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Reverse engineering; Node (physics); Outsourcing; Computer science; Integrated circuit; Process (computing); Physical design; Semiconductor industry; Trojan; Semiconductor device fabrication; Embedded system; Hardware security module; Systems engineering; Computer hardware; Manufacturing engineering; Engineering; Electrical engineering; Operating system; Computer security; Cryptography","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":[0.002706108,0.0009384634,0.0006573046,0.001411802,0.0004412144,0.001492947,0.002011698,0.001048531,0.003677583],"category_scores_gemma":[0.01049356,0.0005646974,0.001080552,0.0006381478,0.00235672,0.002325403,0.001589759,0.001267997,0.00105847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001065782,"about_ca_system_score_gemma":0.001724763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001809146,"about_ca_topic_score_gemma":0.002234808,"domain_scores_codex":[0.9970545,0.0006978018,0.0001112628,0.0004010365,0.001547071,0.0001884479],"domain_scores_gemma":[0.9929183,0.00199051,0.0008039811,0.003246344,0.0009746289,0.00006621266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002478217,0.0001409104,0.004367155,0.0006987603,0.0001572319,0.000471022,0.0002865074,0.4654457,0.1219792,0.1972153,0.006317944,0.2026725],"study_design_scores_gemma":[0.00003572332,0.0003010259,0.001050796,0.00008402018,0.00004232288,0.0005258663,0.00006600992,0.8019565,0.1294328,0.0530771,0.01337142,0.0000564826],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01155934,0.0001656969,0.9802249,0.00007731332,0.00004676656,0.00005634687,0.0001629834,0.005131866,0.002574744],"genre_scores_gemma":[0.4274905,0.0004052581,0.5641796,0.0002200876,0.00003965091,0.0001867578,0.0009972749,0.001585321,0.004895533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003677583,"threshold_uncertainty_score":0.01431143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02027812872747809,"score_gpt":0.2430987976648578,"score_spread":0.2228206689373797,"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."}}