Optimal design of Σ-Δ MEMS accelerometer based on multi-objective genetic algorithm

WANG Meng-xing; LIU Dan; XIONG Xing-yin; LI Zong-wei; HAN Ke-du

doi:10.13873/j.1000-9787(2018)05-0064-03

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Optimal design of Σ-Δ MEMS accelerometer based on multi-objective genetic algorithm

更新时间：2025-09-11

- Optimal design of Σ-Δ MEMS accelerometer based on multi-objective genetic algorithm
- Issue 5, Pages: 64-66+69(2018)
- 作者机构：
  
  1. 中国科学院地质与地球物理研究所中国科学院油气资源研究重点实验室
  2. 中国科学院大学
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2018)05-0064-03
  CLC： TH824.4;TP18
- Published：2018
- 稿件说明：
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WANG Meng-xing, LIU Dan, XIONG Xing-yin, et al. Optimal design of Σ-Δ MEMS accelerometer based on multi-objective genetic algorithm[J]. 2018, (5): 64-66+69.
DOI：

WANG Meng-xing, LIU Dan, XIONG Xing-yin, et al. Optimal design of Σ-Δ MEMS accelerometer based on multi-objective genetic algorithm[J]. 2018, (5): 64-66+69. DOI： 10.13873/j.1000-9787(2018)05-0064-03.

摘要

针对单目标遗传算法设计优化高阶Σ-Δ微机电系统(Σ-ΔMEMS)加速度计时易出现的稳定性问题

提出了基于多目标遗传算法的MEMS加速度计环路滤波器优化设计方法。对三阶非限定性Σ-ΔMEMS加速度计系统

采用多目标遗传算法

将∞—范数和信噪比作为设计目标对其环路滤波器参数进行优化设计。结果表明:相比只针对信噪比进行优化的传统单目标遗传算法

多目标遗传算法在确保高信噪比的同时

提高了系统的相位裕度

使得最大稳定输入信号范围增幅超过1倍

增强了系统对MEMS敏感元件工艺误差的鲁棒性。

Abstract

To address the stability problem which occurs in high-order Σ-Δ micro-electro-mechanical system（ MEMS） accelerometer design using single-objective genetic algorithm

a MEMS accelerometer loop filter optimal design methodology based on multi-objective genetic algorithm（ MOGA） is proposed. For 3 rd-order non-finiteΣ-Δ MEMS accelerometer system

MOGA is adopted

loop filter parameters are designed and optimized using both signal to noise ratio（ SNR） and infinity norm as design targets.Compared to single-objective genetic algorithm which optimize only for SNR

MOGA improves phase margin significantly

double the maximum stable input signal range

and enhance system robustness to technique error of MEMS sensitive element

under the premise of high SNR.

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