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储能系统技术 储能系统 ★ 4.0

基于固定梁的MEMS微波功率检测芯片及其模型

MEMS microwave power detection chip based on fixed beams and its model

作者 Qirui XuZhiyin DingDebo Wang
期刊 半导体学报
出版日期 2025年1月
卷/期 第 46 卷 第 6 期
技术分类 储能系统技术
技术标签 储能系统
相关度评分 ★★★★ 4.0 / 5.0
关键词 Qirui Xu Zhiyin Ding Debo Wang 半导体学报(英文版) Journal of Semiconductors
语言:

中文摘要

针对MEMS悬臂梁过载功率低及传统固定梁灵敏度低的问题,设计了一种基于双导向固定梁结构的新型MEMS微波功率检测芯片。通过在导向梁与测量电极间设置间隙,加速牺牲层释放,有效提升器件性能。提出了该芯片的负载传感模型,并基于均布载荷分析其力学特性。利用该模型研究了过载功率与灵敏度,理论与实验结果吻合良好。芯片在9–11 GHz频段内具有优良微波特性,回波损耗小于-10 dB;在10 GHz时,理论灵敏度为13.8 fF/W,实测值为14.3 fF/W,相对误差仅3.5%。结果表明,所提模型为MEMS微波功率检测芯片的设计与优化提供了有力理论支持。

English Abstract

In order to solve the problems of low overload power in MEMS cantilever beams and low sensitivity in traditional MEMS fixed beams,a novel MEMS microwave power detection chip based on the dual-guided fixed beam is designed.A gap between guiding beams and measuring electrodes is designed to accelerate the release of the sacrificial layer,effectively enhanc-ing chip performance.A load sensing model for the MEMS fixed beam microwave power detection chip is proposed,and the mechanical characteristics are analyzed based on the uniform load applied.The overload power and sensitivity are investi-gated using the load sensing model,and experimental results are compared with theoretical results.The detection chip exhibits excellent microwave characteristic in the 9-11 GHz frequency band,with a return loss less than-10 dB.At a signal fre-quency of 10 GHz,the theoretical sensitivity is 13.8 fF/W,closely matching the measured value of 14.3 fF/W,with a relative error of only 3.5%.These results demonstrate that the proposed load sensing model provides significant theoretical support for the design and performance optimization of MEMS microwave power detection chips.
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SunView 深度解读

该MEMS微波功率检测技术对阳光电源功率变换系统具有重要应用价值。在ST系列储能变流器和SG光伏逆变器中,高频开关器件(SiC/GaN)工作频率可达数十kHz至MHz级,其微波辐射功率检测对EMI抑制和系统可靠性至关重要。该芯片在9-11GHz频段的高灵敏度(14.3fF/W)和低回波损耗特性,可集成于功率模块的在线监测系统,实时检测高频谐波功率泄漏。其双导向固定梁结构兼顾高灵敏度与抗过载能力,适用于PowerTitan大型储能系统的电磁兼容性监测。该负载传感模型为开发适配阳光电源三电平拓扑的定制化功率传感器提供了理论基础,可优化iSolarCloud平台的智能诊断功能,实现功率器件健康状态的预测性维护。