{"id":"W4407736358","doi":"10.1109/jsen.2025.3539571","title":"AI-Driven Device Fingerprinting Using On-Chip Monitoring Sensors: A Novel Time Series-Based Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Integrated Circuits and Semiconductor Failure Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto Nacional de Ciberseguridad; Ontario Ministry of Research, Innovation and Science","keywords":"Series (stratigraphy); Computer science; Chip; Fingerprint recognition; Embedded system; Electronic engineering; Engineering; Fingerprint (computing); Artificial intelligence; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004133563,0.0007064874,0.0005377872,0.0008244617,0.0001976997,0.000602907,0.001102123,0.0006950013,0.0009997821],"category_scores_gemma":[0.001011092,0.0002260808,0.0005586583,0.0006699616,0.0002949172,0.000668716,0.0005383275,0.0007289349,0.000374814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003819001,"about_ca_system_score_gemma":0.0004335339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002388569,"about_ca_topic_score_gemma":0.002721944,"domain_scores_codex":[0.9997627,0.00003727832,0.00001094468,0.00007928618,0.00007677524,0.00003304279],"domain_scores_gemma":[0.9996251,0.0001363828,0.00008223176,0.00004222607,0.00008375701,0.0000302911],"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":[0.0002769962,0.0003678248,0.006154088,0.0002177997,0.0001806538,0.0003905566,0.0001471624,0.5617506,0.04106037,0.007700864,0.00294961,0.3788035],"study_design_scores_gemma":[0.000002071826,0.00002385395,0.00056071,0.000003633495,0.000007147653,0.00003422664,0.000009154202,0.9961659,0.001742037,0.001042112,0.0004050144,0.000004246249],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06400453,0.0008573025,0.9297696,0.0003495421,0.0001086592,0.00006803454,0.000222837,0.001360111,0.003259431],"genre_scores_gemma":[0.849144,0.0004464948,0.1453404,0.000235482,0.0001473524,0.00009917353,0.0004544238,0.00009480033,0.004037797],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002388569,"threshold_uncertainty_score":0.004749358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0226080271695826,"score_gpt":0.252442035703049,"score_spread":0.2298340085334664,"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."}}