{"id":"W3107346677","doi":"10.1021/acsami.0c10796","title":"From Memristive Materials to Neural Networks","year":2020,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada","keywords":"Memristor; Neuromorphic engineering; Computer science; Artificial neural network; Resistive random-access memory; Process (computing); Computer architecture; Artificial intelligence; Electronic engineering; Electrical engineering; Engineering; Voltage","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001443841,0.0003914009,0.0004080058,0.0006073545,0.0002353644,0.0008824171,0.0006781876,0.001079755,0.004700854],"category_scores_gemma":[0.000412741,0.000225998,0.0002364052,0.0004762434,0.0008408982,0.001547642,0.0005542767,0.001290198,0.001203591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004427746,"about_ca_system_score_gemma":0.0003029385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002947311,"about_ca_topic_score_gemma":0.0003758934,"domain_scores_codex":[0.9999039,0.00002053833,0.000006927957,0.0000225153,0.00003505513,0.00001110806],"domain_scores_gemma":[0.9999338,0.00003150139,0.000006775643,0.000006240889,0.00001562631,0.000006040127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000511188,0.0000611006,0.0002813473,0.003642484,0.00004239174,0.0004497883,0.000257541,0.00640777,0.01465738,0.7520308,0.02881029,0.193308],"study_design_scores_gemma":[0.00001216652,0.00008609961,0.0003538351,0.001100771,0.00001981859,0.0005469054,0.000110324,0.0118392,0.007359453,0.4459015,0.5326368,0.00003302231],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.009907593,0.807364,0.05625508,0.009286044,0.004436842,0.00005382385,0.0003573485,0.0003801221,0.1119591],"genre_scores_gemma":[0.1643539,0.7522741,0.02754982,0.003807321,0.004838027,0.0001714459,0.0002909268,0.000100441,0.04661407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004700854,"threshold_uncertainty_score":0.01572597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01640323548980665,"score_gpt":0.2262435731159542,"score_spread":0.2098403376261475,"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."}}