{"id":"W4403331456","doi":"10.1109/access.2024.3478832","title":"An Open-Source AMS Circuit Optimization Framework Based on Reinforcement Learning—From Specifications to Layouts","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Open source; Artificial intelligence; Computer architecture; Programming language; Software","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":[0.0006501621,0.0006791018,0.0005440711,0.000377605,0.0002409092,0.0006370221,0.001504173,0.0008010586,0.005013085],"category_scores_gemma":[0.001592075,0.0003227917,0.0006593717,0.0002642356,0.0005485323,0.0006046135,0.0007802738,0.001022863,0.0007319207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007619351,"about_ca_system_score_gemma":0.001146939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003864766,"about_ca_topic_score_gemma":0.005017925,"domain_scores_codex":[0.9996572,0.00007254738,0.00001254948,0.00006403628,0.0001576973,0.00003605797],"domain_scores_gemma":[0.999663,0.0001448074,0.00003792367,0.00004233807,0.00008594093,0.00002608542],"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":[0.00003007957,0.00002842605,0.0002527039,0.00004358688,0.00001668132,0.00003924849,0.00001821173,0.9416985,0.002650758,0.008870548,0.00102861,0.04532271],"study_design_scores_gemma":[0.00000760007,0.00001297281,0.00002476537,0.000002308642,0.000002205369,0.000008195323,0.000001402743,0.9964458,0.0006144664,0.001991323,0.0008866481,0.000002315262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00544711,0.00009366431,0.9885532,0.00007697066,0.00002466262,0.00003814764,0.00006570754,0.002834893,0.002865662],"genre_scores_gemma":[0.4537093,0.0001691259,0.5397897,0.0001441456,0.00005561648,0.0002480983,0.0002430398,0.0007992747,0.004841676],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005013085,"threshold_uncertainty_score":0.01677042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06595372534878907,"score_gpt":0.3090599298475921,"score_spread":0.243106204498803,"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."}}