{"id":"W2802897963","doi":"10.1109/iscas.2018.8351459","title":"Optimizing an Analog Neuron Circuit Design for Nonlinear Function Approximation","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Office of Naval Research","keywords":"Subthreshold conduction; Computer science; CMOS; Transistor; Offset (computer science); Nonlinear system; Electronic engineering; Digital electronics; Threshold voltage; Electronic circuit; Topology (electrical circuits); Voltage; Electrical engineering; Physics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001443137,0.0003084198,0.0001617183,0.000165146,0.0001929611,0.0003996222,0.0006796758,0.0002511844,0.002002888],"category_scores_gemma":[0.0003554535,0.0001455634,0.0001726583,0.0002020421,0.0001485892,0.0003640687,0.0002037521,0.0002394336,0.0003939628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006541041,"about_ca_system_score_gemma":0.0007857349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009466165,"about_ca_topic_score_gemma":0.003481378,"domain_scores_codex":[0.999894,0.00001044418,0.000008015755,0.00002673466,0.00004371846,0.00001707891],"domain_scores_gemma":[0.9998621,0.00003001168,0.00002760795,0.00001756767,0.00005502643,0.000007563576],"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":[0.0001772198,0.0001320902,0.001687485,0.0003132345,0.00008797331,0.0001485084,0.00009952088,0.176324,0.6875195,0.0109082,0.00213143,0.120471],"study_design_scores_gemma":[0.00005761963,0.0006285955,0.001504557,0.00002126803,0.00008784178,0.0002317019,0.00004432359,0.7093836,0.2752322,0.002518879,0.01026889,0.00002061083],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2870257,0.000484702,0.6949767,0.0003414674,0.0001087486,0.0001675287,0.0001997502,0.001154288,0.01554115],"genre_scores_gemma":[0.7590777,0.0001345753,0.2372633,0.000108821,0.0000137298,0.00008986116,0.00007451094,0.00006678271,0.003170769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002002888,"threshold_uncertainty_score":0.006700337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05991287993106981,"score_gpt":0.2616390379597056,"score_spread":0.2017261580286357,"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."}}