{"id":"W4280571518","doi":"10.23919/date54114.2022.9774699","title":"Deep Reinforcement Learning for Analog Circuit Structure Synthesis","year":2022,"lang":"en","type":"article","venue":"2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Computer science; Reinforcement learning; Construct (python library); Set (abstract data type); Reliability (semiconductor); Circuit extraction; Circuit design; Computer engineering; Artificial intelligence; Equivalent circuit; Engineering; Electrical engineering; Embedded system; Programming language; Voltage","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000490039,0.000529746,0.0004363685,0.000308764,0.0002122333,0.0003406207,0.0008158094,0.0004542066,0.00229276],"category_scores_gemma":[0.0009212659,0.0002509721,0.0003340839,0.0002270758,0.0004604149,0.0004892738,0.0005142922,0.000905516,0.0002885421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007491701,"about_ca_system_score_gemma":0.0007706577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002729878,"about_ca_topic_score_gemma":0.003703347,"domain_scores_codex":[0.9997824,0.00004729785,0.00001056326,0.00004074142,0.00008814401,0.00003082033],"domain_scores_gemma":[0.9996998,0.0001523378,0.00003182049,0.00003470787,0.00006533565,0.00001596951],"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":[0.00004372718,0.00004768292,0.0003120085,0.00007002053,0.00002435312,0.00004376298,0.0000327607,0.8405712,0.008737355,0.01199579,0.0006916832,0.1374297],"study_design_scores_gemma":[0.000004678449,0.00001833202,0.00002414497,0.000002645435,0.000002324555,0.000005900525,0.000001245499,0.9961042,0.001159038,0.002284882,0.0003908232,0.000001706637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007134455,0.0001537921,0.9902388,0.00005092224,0.00001636355,0.00002015663,0.00001399621,0.0004426537,0.001928824],"genre_scores_gemma":[0.6711366,0.0001710355,0.3243397,0.0001078716,0.00002680576,0.0001286383,0.00007336956,0.00009325541,0.00392267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002729878,"threshold_uncertainty_score":0.007670045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04201048525884097,"score_gpt":0.2475071451572473,"score_spread":0.2054966598984063,"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."}}