{"id":"W4409064026","doi":"10.1038/s41467-025-58231-5","title":"ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Office of Energy Efficiency; U.S. Department of Energy; Office of Energy Efficiency and Renewable Energy; European Commission; Division of Electrical, Communications and Cyber Systems; HORIZON EUROPE Framework Programme; Office of Science; Directorate for Engineering; SLAC National Accelerator Laboratory; Directorate for Computer and Information Science and Engineering; Bundesministerium für Bildung und Forschung; National Science Foundation","keywords":"Neuromorphic engineering; Computer science; Ising model; Nanotechnology; Neuroscience; Materials science; Physics; Condensed matter physics; Artificial intelligence; Biology; Artificial neural 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":[],"consensus_categories":[],"category_scores_codex":[0.0002296772,0.000171106,0.0001734387,0.0002182909,0.0009994534,0.0002525605,0.003714137,0.0001727407,0.000002719998],"category_scores_gemma":[0.00013978,0.0001518328,0.0001041577,0.001277895,0.00009630917,0.00022635,0.001720206,0.001325759,0.000007900873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004924168,"about_ca_system_score_gemma":0.00008869098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002299544,"about_ca_topic_score_gemma":0.00006478824,"domain_scores_codex":[0.998705,0.0002422164,0.0002535937,0.0003351861,0.0001827413,0.0002812508],"domain_scores_gemma":[0.9962254,0.0007748016,0.0001091446,0.002685739,0.0001396599,0.00006522715],"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":[0.00001841853,0.0004852512,0.004117414,0.00004741125,0.0001431977,0.00003046435,0.0003707514,0.1181776,0.002037033,0.7046432,0.03283536,0.1370939],"study_design_scores_gemma":[0.0001629288,0.00001938833,0.001984611,0.0001328071,0.00001059759,0.0000130963,0.0000060279,0.9702818,0.00006083128,0.005073091,0.02209763,0.0001571993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3706265,0.06826004,0.3432123,0.1530062,0.009220576,0.001639805,0.00002072913,0.002233665,0.05178022],"genre_scores_gemma":[0.9611942,0.0002052904,0.03490565,0.003441218,0.00006123702,0.000005232625,0.000006801031,0.00001140025,0.0001690041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8521042,"threshold_uncertainty_score":0.7687088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03882129361930014,"score_gpt":0.3206198904818194,"score_spread":0.2817985968625193,"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."}}