{"id":"W3175542906","doi":"10.1016/j.mee.2021.111706","title":"Fully CMOS-compatible passive TiO2-based memristor crossbars for in-memory computing","year":2022,"lang":"en","type":"article","venue":"Microelectronic Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Neuromorphic engineering; Crossbar switch; Memristor; Computer science; Fabrication; CMOS; Computer architecture; Voltage; Process (computing); Resistive random-access memory; Electronic engineering; Artificial neural network; Materials science; Electrical engineering; Optoelectronics; Engineering; Telecommunications; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001902978,0.0003012388,0.0003299316,0.000279379,0.0002684521,0.00003165704,0.0002974451,0.00005037033,0.0000281908],"category_scores_gemma":[0.000029721,0.0003957823,0.0001285322,0.0004991569,0.00001513958,0.00008088959,0.0000752792,0.0006740328,0.000003748966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009451995,"about_ca_system_score_gemma":0.00007235754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004979418,"about_ca_topic_score_gemma":0.00001003564,"domain_scores_codex":[0.9981947,0.0000221934,0.0003842053,0.0003456068,0.0001507876,0.0009025294],"domain_scores_gemma":[0.9994066,0.0002125041,0.00005479061,0.0002266016,0.00002572494,0.00007378357],"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":[0.00001789161,0.00001622358,0.00001476606,0.0001217885,0.00001443697,0.00001499946,0.0001315829,0.7500268,0.2449422,0.00006894619,0.0001111485,0.004519301],"study_design_scores_gemma":[0.001150596,0.0001313756,0.00008273485,0.00004068232,0.00001121797,0.00003627269,0.00006392489,0.7520157,0.2339142,0.00003391753,0.0120564,0.000462918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8049295,0.001907886,0.1909014,0.00005343631,0.0007197171,0.000544125,0.00002067662,0.0007829927,0.0001402117],"genre_scores_gemma":[0.9955,0.000005734157,0.003937264,0.00007253425,0.0001419665,0.000125682,0.00003828367,0.0001118294,0.0000666528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1905705,"threshold_uncertainty_score":0.9998494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005836960069177837,"score_gpt":0.2061945273375565,"score_spread":0.2003575672683787,"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."}}