{"id":"W2053448087","doi":"10.1109/rme.2009.5201375","title":"Cross-coupled bit-line biasing for 22-nm SRAM","year":2009,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Static random-access memory; Biasing; Monte Carlo method; CMOS; Electronic engineering; Line (geometry); Random access memory; Computer science; Voltage; Electrical engineering; Engineering; Computer hardware; Mathematics; Statistics","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":[0.0001360859,0.0002071,0.0001144516,0.0001362566,0.0002083321,0.0002551995,0.0005072878,0.0002434816,0.0007595873],"category_scores_gemma":[0.0004113788,0.0001321987,0.0001168073,0.0001854084,0.0001821541,0.0003214212,0.0002118898,0.0001901501,0.0001625577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003049253,"about_ca_system_score_gemma":0.0002514621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001136926,"about_ca_topic_score_gemma":0.002947358,"domain_scores_codex":[0.9999321,0.00001472521,0.000004374492,0.00001270793,0.0000298072,0.00000632024],"domain_scores_gemma":[0.9998317,0.00004214386,0.00004181087,0.00003196988,0.00004393043,0.000008340272],"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":[0.0002674568,0.0001103812,0.004581343,0.0002832637,0.00005594076,0.0003200673,0.0001508114,0.08724004,0.8157729,0.007207963,0.0008842573,0.08312561],"study_design_scores_gemma":[0.00004721836,0.0008969058,0.0048348,0.00003612442,0.00009401208,0.0006579132,0.00003723422,0.5333384,0.4483257,0.001425191,0.01026061,0.00004593981],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7643108,0.002463789,0.2268042,0.0002440242,0.0001138712,0.00009267256,0.00007599794,0.0006145331,0.005280105],"genre_scores_gemma":[0.9745702,0.0002893965,0.0243066,0.00004975633,0.00001152255,0.00001685967,0.00002130061,0.00002076741,0.0007136791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001136926,"threshold_uncertainty_score":0.002541125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01634166263087703,"score_gpt":0.267259761010161,"score_spread":0.2509180983792839,"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."}}