{"id":"W4386361455","doi":"10.1109/nano58406.2023.10231266","title":"QuickSim: Efficient and Accurate Physical Simulation of Silicon Dangling Bond Logic","year":2023,"lang":"en","type":"article","venue":"","topic":"Advancements in Semiconductor Devices and Circuit Design","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pace; Computer science; CMOS; Automation; Dissipation; Dangling bond; Computer engineering; Electronic design automation; Field (mathematics); Domain (mathematical analysis); Logic synthesis; Logic gate; Electronic engineering; Silicon; Embedded system; Algorithm; Engineering; Materials science; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005953289,0.00008124708,0.0001153112,0.00004588905,0.0000232708,0.000009882261,0.00004594496,0.00002703389,0.00001663422],"category_scores_gemma":[0.00001235806,0.00007178239,0.00002004657,0.0001876339,0.00002006569,0.00006155209,0.0000163914,0.00004783443,0.00001744738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001324625,"about_ca_system_score_gemma":0.000002538886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001616084,"about_ca_topic_score_gemma":4.919173e-7,"domain_scores_codex":[0.9995295,0.000005363998,0.0001278502,0.0001107847,0.00009159203,0.0001349066],"domain_scores_gemma":[0.9997275,0.0001016813,0.00002207325,0.0001008591,0.00001882926,0.00002905982],"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":[0.000001311082,0.000008079891,0.00008965629,0.00004833335,0.000009495262,9.920723e-7,0.0002814139,0.9321226,0.06326178,0.0009338537,0.0000330512,0.003209388],"study_design_scores_gemma":[0.0001431821,0.00001816315,0.0003248529,0.00001301687,0.000007977628,2.805423e-7,0.000253398,0.9823556,0.01596645,0.0006102792,0.0002182194,0.00008855847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855174,0.0001062489,0.01172902,0.000004077796,0.0001223489,0.00009872824,0.00000371617,0.0002179683,0.002200492],"genre_scores_gemma":[0.9997825,0.00001957118,0.00005505088,0.00001387283,0.00004492582,0.000003661715,0.000005990732,0.00001340884,0.00006099759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05023297,"threshold_uncertainty_score":0.29272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03280990640720814,"score_gpt":0.2907200798290849,"score_spread":0.2579101734218768,"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."}}