电子学报2025，Vol.53Issue(6)：1784-1791,8.DOI:10.12263/DZXB.20250055
基于人工智能算法的单级全差分折叠式共源共栅运算放大器的多目标设计方法
Multi-Objective Design Method for Single-Stage Fully Differential Folded Cascode Operational Amplifiers Based on the Artificial Intelligence Algorithm
摘要
Abstract
With the advancement of integrated circuit manufacturing technology,analog integrated circuit design fac-es the challenge of trade-offs between performance metrics such as power consumption and gain.Traditional design meth-ods,reliant on approximate equations and iterative refinement,are inefficient.This paper presents an artificial intelligence algorithm-based multi-objective design strategy for the design of a single-stage fully differential folded cascode operational amplifier.This method employs a neural network model to characterize the mapping relationship between design parame-ters and eight performance metrics,and sets the target performance for the operational amplifier to achieve through fitness functions and constraint conditions,then utilizes particle swarm optimization(PSO)algorithm to search for the optimal fit-ness.Experimental results show that multiple metrics exceed design targets,with a maximum voltage gain of 65 dB and a phase margin of 74°.Using this method,we can quickly and accurately obtain operational amplifier parameters that meet de-sign specifications.Compared to manual calculations,this method reduces the running time to merely 906 seconds,signifi-cantly improving the design efficiency.It can be applied to more large-scale circuit designs in the future.关键词
人工智能算法/全差分折叠式共源共栅运算放大器/多目标设计/神经网络模型/粒子群优化Key words
artificial intelligence algorithms/fully differential folded cascode operational amplifier/multi-objective design/neural network model/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
李照希,苏震宇,田宇浩,侯琛雪,杨银堂..基于人工智能算法的单级全差分折叠式共源共栅运算放大器的多目标设计方法[J].电子学报,2025,53(6):1784-1791,8.基金项目
国家自然科学基金(No.62304165) （No.62304165）
中国博士后科学基金专项资金(No.2024T170691) （No.2024T170691）
中国博士后科学基金(No.2023M732745) （No.2023M732745）
国家博士后资助计划(No.GZC20232024) （No.GZC20232024）
陕西省博士后科研项目(No.30102230001) National Natural Science Foundations of China(No.62304165) （No.30102230001）
China Postdoctoral Science Foundation Special Funding(No.2024T170691) （No.2024T170691）
China Postdoctoral Science Foundation(No.2023M732745) （No.2023M732745）
National Funded Postdoctoral Program of China(No.GZC20232024) （No.GZC20232024）
Shaanxi Province Postdoctoral Scientific Research Project Grant(No.30102230001) （No.30102230001）
