Jintao Li, Yanhan Zeng, Hailong Wu, R. Li, Jun Zhang, Hongzhou Tan
{"title":"Performance optimization for LDO regulator based on the differential evolution","authors":"Jintao Li, Yanhan Zeng, Hailong Wu, R. Li, Jun Zhang, Hongzhou Tan","doi":"10.1109/ASICON47005.2019.8983642","DOIUrl":null,"url":null,"abstract":"An application of differential evolution for parameter optimization in the low dropout regulator (LDO) is presented in this paper. The parameters optimization by manual work for the analog integrated circuit, such as LDO, is laborious and time-consuming, and it is uncertain to find the relatively good result. In this paper, the differential evolution is used to optimize the parameters and find the relatively good performance of LDO. In order to improve the convergence speed and optimization effect, a new constraint solution and a fast weight-based non-dominated sorting method are proposed. Simulation results show that the gain-bandwidth product,load regulation and line regulation are improved by 206.5%, 58.1% and 87.6%, respectively, compared with the manual solution.","PeriodicalId":319342,"journal":{"name":"2019 IEEE 13th International Conference on ASIC (ASICON)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 13th International Conference on ASIC (ASICON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASICON47005.2019.8983642","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
An application of differential evolution for parameter optimization in the low dropout regulator (LDO) is presented in this paper. The parameters optimization by manual work for the analog integrated circuit, such as LDO, is laborious and time-consuming, and it is uncertain to find the relatively good result. In this paper, the differential evolution is used to optimize the parameters and find the relatively good performance of LDO. In order to improve the convergence speed and optimization effect, a new constraint solution and a fast weight-based non-dominated sorting method are proposed. Simulation results show that the gain-bandwidth product,load regulation and line regulation are improved by 206.5%, 58.1% and 87.6%, respectively, compared with the manual solution.