MEMS压电式水听器需要使用模拟前端对产生的信号进行预处理,但当前水听器的模拟前端大都基于板级方案,检测性能、集成度和适用范围有待提高,需要设计一款模拟前端专用芯片实现MEMS压电式水听器系统的微型化、阵列化。首先根据MEMS压电...MEMS压电式水听器需要使用模拟前端对产生的信号进行预处理,但当前水听器的模拟前端大都基于板级方案,检测性能、集成度和适用范围有待提高,需要设计一款模拟前端专用芯片实现MEMS压电式水听器系统的微型化、阵列化。首先根据MEMS压电式水听器的特点和需求,确定了芯片的系统架构;其次设计了自动增益控制环路、可编程增益放大器和频带可变低通滤波器,拓宽了芯片的适用范围;最后将斩波结合自调零的降噪技术用于芯片,以满足水听器低噪声测量的要求。基于0.18μm CMOS工艺对模拟前端芯片进行设计,后仿真结果显示所设计电路在1 k Hz时等效输入噪声功率谱密度为59 n V/√Hz,输入电荷量与输出电压的非线性误差小于1.26%。验证了模拟前端专用芯片可以对MEMS压电式水听器产生的低频微弱信号实现低噪声放大。展开更多
The bias of micro-electro-mechanical system(MEMS)gyroscopes is sensitive to temperature variations,which limits their accuracy in complex thermal environments.To address this issue,this paper proposes a Gaussian proce...The bias of micro-electro-mechanical system(MEMS)gyroscopes is sensitive to temperature variations,which limits their accuracy in complex thermal environments.To address this issue,this paper proposes a Gaussian process regression(GPR)model that uses resonant frequency and quadrature output as inputs to predict and compensate for the full-temperature bias of MEMS gyroscopes in real-time.Without relying on external sensors,the resonant frequency and quadrature output serve as virtual sensors that directly reflect bias variations.To suppress noise and improve modeling accuracy,the bias is preprocessed using particle swarm optimization-optimized variational mode decomposition before training.In addition,a fast computation strategy is developed to improve the computational efficiency of the GPR model.Experimental results demonstrate the effectiveness and superiority of the proposed method.In three repeated trials,the bias instability of the compensated bias is reduced by 46.18%,60.18%,and 63.68%,respectively,compared to the uncompensated bias.展开更多
摘要MEMS压电式水听器需要使用模拟前端对产生的信号进行预处理,但当前水听器的模拟前端大都基于板级方案,检测性能、集成度和适用范围有待提高,需要设计一款模拟前端专用芯片实现MEMS压电式水听器系统的微型化、阵列化。首先根据MEMS压电式水听器的特点和需求,确定了芯片的系统架构;其次设计了自动增益控制环路、可编程增益放大器和频带可变低通滤波器,拓宽了芯片的适用范围;最后将斩波结合自调零的降噪技术用于芯片,以满足水听器低噪声测量的要求。基于0.18μm CMOS工艺对模拟前端芯片进行设计,后仿真结果显示所设计电路在1 k Hz时等效输入噪声功率谱密度为59 n V/√Hz,输入电荷量与输出电压的非线性误差小于1.26%。验证了模拟前端专用芯片可以对MEMS压电式水听器产生的低频微弱信号实现低噪声放大。
基金supported by the National Natural Science Foundation of China(No.12172180).
摘要The bias of micro-electro-mechanical system(MEMS)gyroscopes is sensitive to temperature variations,which limits their accuracy in complex thermal environments.To address this issue,this paper proposes a Gaussian process regression(GPR)model that uses resonant frequency and quadrature output as inputs to predict and compensate for the full-temperature bias of MEMS gyroscopes in real-time.Without relying on external sensors,the resonant frequency and quadrature output serve as virtual sensors that directly reflect bias variations.To suppress noise and improve modeling accuracy,the bias is preprocessed using particle swarm optimization-optimized variational mode decomposition before training.In addition,a fast computation strategy is developed to improve the computational efficiency of the GPR model.Experimental results demonstrate the effectiveness and superiority of the proposed method.In three repeated trials,the bias instability of the compensated bias is reduced by 46.18%,60.18%,and 63.68%,respectively,compared to the uncompensated bias.