Multi-Objective Design Method for Single-Stage Fully Differential Folded Cascode Operational Amplifiers Based on the Artificial Intelligence Algorithm

LI Zhao-xi; SU Zhen-yu; TIAN Yu-hao; HOU Chen-xue; YANG Yin-tang

doi:10.12263/DZXB.20250055

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Multi-Objective Design Method for Single-Stage Fully Differential Folded Cascode Operational Amplifiers Based on the Artificial Intelligence Algorithm

Large-Scale Models and the Internet | 更新时间：2025-10-16

- Multi-Objective Design Method for Single-Stage Fully Differential Folded Cascode Operational Amplifiers Based on the Artificial Intelligence Algorithm
- ACTA ELECTRONICA SINICA Vol. 53, Issue 6, Pages: 1784-1791(2025)
- 作者机构：
  
  西安电子科技大学集成电路学部，陕西西安 710126
- 作者简介：
- 基金信息：
  
  National Natural Science Foundations of China(62304165);China Postdoctoral Science Foundation Special Funding(2024T170691);China postdoctoral science foundation(2023M732745);National Funded Postdoctoral Program of China(GZC20232024);Shaanxi Province Postdoctoral Scientific Research Project Grant(30102230001)
- DOI：10.12263/DZXB.20250055
  CLC： TN40;TN43
- Received：22 May 2025，
  
  Revised：2025-06-05，
  
  Published：25 June 2025
- 稿件说明：
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李照希, 苏震宇, 田宇浩, 等. 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(06): 1784-1791.

LI Zhao-xi, SU Zhen-yu, TIAN Yu-hao, et al. Multi-Objective Design Method for Single-Stage Fully Differential Folded Cascode Operational Amplifiers Based on the Artificial Intelligence Algorithm[J]. Acta Electronica Sinica, 2025, 53(06): 1784-1791.
李照希, 苏震宇, 田宇浩, 等. 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(06): 1784-1791. DOI：10.12263/DZXB.20250055

LI Zhao-xi, SU Zhen-yu, TIAN Yu-hao, et al. Multi-Objective Design Method for Single-Stage Fully Differential Folded Cascode Operational Amplifiers Based on the Artificial Intelligence Algorithm[J]. Acta Electronica Sinica, 2025, 53(06): 1784-1791. DOI：10.12263/DZXB.20250055

摘要

随着集成电路制造技术的发展，模拟集成电路设计面临着功耗、增益等性能指标折中的挑战.传统的设计方法依赖于近似方程和反复迭代，导致效率低下.本文提出了一种基于人工智能算法的多目标设计策略，用于单级全差分折叠式共源共栅运算放大器的设计.该方法采用神经网络模型来表征设计参数与8个性能指标之间的映射关系，并通过适应度函数和约束条件设定运放所需达成的目标性能，再使用粒子群优化（Particle Swarm Optimization，PSO）算法搜寻最佳适应度.实验结果表明，多项指标均优于设计目标，其中最大电压增益达到了65 dB，相位裕度为74°.利用该方法，能够快速且准确地获得满足设计要求的运放参数.与手工计算相比，该方法的运行时间仅为906 s，显著提高了设计效率，未来可应用于更多大规模电路设计中.

Abstract

With the advancement of integrated circuit manufacturing technology

analog integrated circuit design faces the challenge of trade-offs between performance metrics such as power consumption and gain. Traditional design methods

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 parameters 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 fitness. 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 design specifications. Compared to manual calculations

this method reduces the running time to merely 906 seconds

significantly improving the design efficiency. It can be applied to more large-scale circuit designs in the future.

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references

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