{"id":"W4224127714","doi":"10.21203/rs.3.rs-1548156/v1","title":"Medium-Temperature-Oxidized GeO Resistive-Switching Random-Access Memory and Its Applicability in Processing-in-Memory Computing","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Seoul National University; National Research Foundation","keywords":"Resistive random-access memory; Von Neumann architecture; Computer science; Interconnection; Latency (audio); Non-volatile memory; Computer architecture; In-Memory Processing; Reliability (semiconductor); Embedded system; Computer hardware; Electrical engineering; Power (physics); Engineering; Operating system; Voltage; Computer network; Search engine; Telecommunications","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":[0.0001478676,0.0001478379,0.0001260757,0.0001416963,0.0001113759,0.000323488,0.0004455207,0.0001970777,0.000956859],"category_scores_gemma":[0.0003052939,0.00005331716,0.00009363074,0.0001898663,0.0001228915,0.0002873123,0.0001465933,0.0001728631,0.0001840273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001818948,"about_ca_system_score_gemma":0.000135333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003159651,"about_ca_topic_score_gemma":0.0005645251,"domain_scores_codex":[0.9999342,0.000008338026,0.000003082426,0.00001456825,0.0000257513,0.00001409654],"domain_scores_gemma":[0.9998907,0.00002049189,0.00001745751,0.00002507354,0.00003950197,0.000006869542],"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":[0.0003057215,0.0001165703,0.002789144,0.0002406183,0.00004009311,0.0003508096,0.00006092788,0.01606308,0.9406454,0.00573155,0.00127171,0.03238431],"study_design_scores_gemma":[0.0000149553,0.0005912237,0.004265197,0.00001550953,0.00004434623,0.0001643781,0.00006101737,0.1505093,0.8402783,0.001177716,0.002864522,0.00001346921],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833295,0.00058129,0.01101206,0.0001025584,0.0000402744,0.00001966698,0.0001409393,0.0004123245,0.004361397],"genre_scores_gemma":[0.9944128,0.00009569906,0.004902545,0.00001177663,0.000003640165,0.000006272109,0.00005752607,0.000009718037,0.0004998762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000956859,"threshold_uncertainty_score":0.003201008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05025410407982473,"score_gpt":0.3833002415743563,"score_spread":0.3330461374945315,"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."}}