{"id":"W4220847857","doi":"10.1002/aisy.202200001","title":"Memristors with Initial Low‐Resistive State for Efficient Neuromorphic Systems","year":2022,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Key Laboratory of Advanced Functional Materials of Jiangsu Province; Collaborative Innovation Center of Suzhou Nano Science and Technology; King Abdullah University of Science and Technology; Priority Academic Program Development of Jiangsu Higher Education Institutions; State Administration of Foreign Experts Affairs; Technology Agency of the Czech Republic; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Neuromorphic engineering; Memristor; Initialization; Computer science; Artificial neural network; Resistive touchscreen; Process (computing); Electronic engineering; Computer architecture; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001502091,0.0002925319,0.0002855511,0.0002562912,0.0002049872,0.0005616174,0.0005136426,0.0004078466,0.003212223],"category_scores_gemma":[0.0005191584,0.0001943382,0.0001887831,0.0002242842,0.0002410783,0.0007736621,0.0003692276,0.0006075557,0.001333096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003103781,"about_ca_system_score_gemma":0.0001304461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00015133,"about_ca_topic_score_gemma":0.000417721,"domain_scores_codex":[0.9999126,0.000008824502,0.000006628367,0.00001720862,0.00003912533,0.00001560442],"domain_scores_gemma":[0.9998642,0.0000324669,0.00003544837,0.00002328897,0.00003048241,0.00001414687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004835962,0.00004945584,0.0001649672,0.0004332739,0.00001167531,0.0001348767,0.00005811661,0.001986965,0.9579599,0.006224777,0.001663271,0.03126441],"study_design_scores_gemma":[0.00001945763,0.000263994,0.000694995,0.00006981342,0.00001622955,0.0002845714,0.00004318466,0.02391011,0.9492146,0.001988232,0.02347688,0.00001799547],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7169819,0.01461615,0.1969618,0.001721993,0.000846639,0.0002762192,0.0007433022,0.004500383,0.06335153],"genre_scores_gemma":[0.9414876,0.00180494,0.04935891,0.00019869,0.0000659053,0.0001006493,0.0001330202,0.0001454969,0.006704813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003212223,"threshold_uncertainty_score":0.01074594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02395605729753216,"score_gpt":0.2406948945503204,"score_spread":0.2167388372527883,"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."}}