Bioinspired Passive Tactile Sensors Enabled by Reversible Polarization of Conjugated Polymers

Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static stimuli.Here,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic stimuli.Specifically,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential differences.The controllable and reversible polarization process of the conjugated polymers is fully in situ characterized.Then,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference outputs.Compared with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior bio-friendliness.As demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning algorithms.This tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins.

Cell-Based Biosensors and Bio-MEMS for Its New Applications

This paper reviews a novel cell-based biosensor and Bio-MEMS which incorporate living cells as sensing elements that convert a change in immediate environment to signals conducive for processing.It is characterized with high sensitivity,excellent selectivity and fast response and have been implemented for a number of applications ranging from pharmaceutical screening to environmental pollutant detection.This paper also introduces our recent work about Light-Addressable Potentiometric Sensors (LAPS),Field Effect Transistor (FET),Micro-Electrode Array Sensors (MEAS) and Bio-MEMS for detecting the changes of concentration of extracellular ions and the action potential of living cell under effect of drugs and environmental parameters.Finely, the paper gives some prospects of cell-based biosensors in the future.

Research on technology cluster evolution of global MEMS sensors based on patent co-occurrence analysis

At present, microelectro mechanical systems (MEMS) sensors have gradually replaced traditional mechanical sensors and are applied to several fields. Many developed countries pay high attention to technological innovation of MEMS sensors, and have applied a large number of patents since 2000. In this study, the patents of MEMS sensor from 2000 to 2015 are researched, the patents data is collected from Derwent Innovation Index (DII), and the method of co-classification analysis is used to investigate the technology cluster evolution of MEMS sensors. Results show that the technology diffusion occurrs in each technical field and the technology relevance between different technical fields is changed over time. On the whole, the evolution process of MEMS sensor is the manufacture and material of sensor chip, the electronic components and measuring function, the computing and control technology, and applications to biochemical field and communication.

Biosensors and Biochips Based on BioMEMS and Nanotechnology

In the past two decades,the biological and medical fields have seen great advances in the development of biosensors and bioehips capable of characterizing and quantifying biomolecules.This lecture is meant to discuss the development and applications of advanced electroanalysis,biophotonics,nanotechnology,MEMS- based biosensors and biochips for biomedical diagnostics and physical performances of athlete.

Output Drifting of Vacuum Packaged MEMS Sensors Due to Room Temperature Helium Exposure

Exposure of absolute pressure sensors, resonant microtube density, binary concentration sensors and chip-scale vacuum packaged pirani gauges to room temperature helium resulted in a gradual drift in sensor output. No effect was found for differential pressure sensors and pirani gauges vacuum packaged with ceramic or metal packages. The observed results apply to other vacuum packaged MEMS devices such as gyroscopes, voltage controlled oscillators, infrared and Coriolis mass flow sensors. Potential causes for this loss of hermeticity are discussed as well as application limitations for MEMS sensors.

基于MEMS技术的集成压力-湿度传感器

设计了一种高灵敏度的集成压力和湿度传感单元的芯片。压力传感单元基于SOI和蛇形电阻结构。室温下,传感器在载压范围为3~129 kPa内灵敏度为0.026 mV/kPa,与有限元仿真基本吻合。传感器在25~120℃范围内的热灵敏度漂移为0.004‰FS/℃,热零点漂移为0.25%FS/℃。湿度传感单元采用的叉指电极和电容式结构,引入含氟PI作湿度敏感膜。设计Mo电阻加热结构加快传感器降湿过程,缩短降湿时间近32%。在10%~90%RH的湿度范围,含氟PI湿度传感器的灵敏度为0.121 pF/%RH,略低于无氟PI器件。含氟基团的引入,使得传感器的湿滞较无氟PI降低16%。“电容-湿度”曲线呈指数分布,相关系数R^(2)=0.996。测试结果发现,湿度传感单元和压力传感单元拥有良好的独立工作性能。

Error analysis and characterization of new MEMS IMU sensors and integration with PPP

Several new MEMS Inertial Measurement Unit(IMU) sensor products have been released recently with improved performance,which have the potential to support much higher precision applications.New MEMS IMUs include the NavChip from InterSense,the Nav440 from Crossbow,the Landmark30/40 from GTI,the SDI500 from Systron Donner.Since they are new in the market,currently there is limited information about their error characterization which however is important for the construction of proper error models for their integration with other sensors such as GPS.This paper will investigate the error characterization of two new MEMS IMU sensors,namely the NavChip and Nav440,using Allan variance technique.In addition to identifying different error terms,different stochastic error modeling methods,such as Gauss-Markov(GM) and Autoregressive(AR) processes,will also be investigated to assess the MEMS IMU sensor biases.Investigation to integrate new MEMS IMU sensors with Precise Point Positioning(PPP) will also be conducted to address the re-convergence issues.

Seismic monitoring network based on MEMS sensors

This paper provides a brief introduction to the application of the sensor monitoring network of micro-electro-mechanical systems(MEMS)to Zhejiang province.In the Wenzhou Shanxi reservoir and other areas,MEMS and traditional intensity-monitoring instruments have been deployed with complementary functions to implement hybrid networking.The low-cost MEMS network can continuously monitor areas at high risk of earthquakes at a high resolution.Moreover,it can quickly collect the parameters of earthquakes and records of the near-field acceleration of strong earthquakes.It can be also used to rapidly generate earthquake intensity reports and provide early warning of earthquakes.We used the MEMS sensors for the first time in 2016,and it has helped promote the development and application of seismic intensity instruments since then.

基于POA-VMD-WT的MEMS去噪方法

针对MEMS传感器所测得的加速度和角速度输出信号噪声较大问题,提出一种基于鹈鹕优化算法(pelican optimization algorithm,POA)的变分模态分解(variational mode decomposition,VMD)结合小波阈值(wavelet threshold,WT)的去噪方法。首先利用POA对VMD的参数组合进行优化选择,然后应用POA-VMD将含噪信号自适应、非递归地分解为一系列本征模态函数(intrinsic mode function,IMF)。再通过计算每个IMF的余弦相似度对IMFs进行分类,根据计算结果将IMFs分为噪声主导分量与信号主导分量,对分类后的噪声主导分量进行改进小波阈值去噪处理,最后对处理后的噪声分量与信号主导分量进行重构,获得降噪后的MEMS传感器信号。静态和动态实验结果表明,该方法去噪处理后信号的信噪比分别提高12和10 dB,均方误差分别降低75.5%和46.6%,去噪效果显著,能够提高MEMS传感器的精度。

GNSS/MEMS IMU车载组合导航中IMU比例因子误差的影响分析

从系统状态模型出发,分析比例因子误差对组合导航精度和计算量的影响,同时基于车载运动的特点分析比例因子误差的观测性,提出一种仅保留航向陀螺仪和水平加速度计比例因子误差的降维状态模型。实验结果表明,当比例因子误差大于6×10^(-3)时,增广比例因子误差有助于提高导航精度,但计算量增加约170%;降维模型能够达到高维模型的导航精度,与不增广比例因子误差相比,计算量仅增加约70%。

基于MEMS陀螺的高温随钻定向测斜仪研究

针对油气井测量时所面临的小孔径、超深井、温度范围广、温度变化率高、工作时间长等挑战,研究了基于MEMS陀螺仪的定向测斜仪,设计了MEMS陀螺定向测斜仪软硬件系统.为确保该环境下的测量精度提出了一种动态温度补偿方案,并采用基于小波滤波多尺度分析的方法对动态温变环境下陀螺输出信号进行去噪滤波处理,以实现定向测斜仪系统的测量精度.论文给出了定向测斜仪硬件系统方案、定向测斜算法、小波多尺度滤波算法.测试结果表明:该系统在常温和动态温变环境下井斜方位角精度优于±2°,井斜角精度优于±0.1°,工作温度最高150℃,可适应我国深层油气井勘探开发的应用场景需求.

基于改进自适应滤波的MEMS陀螺振动误差抑制研究

本文提出一种基于改进自适应滤波的MEMS陀螺随机振动误差的补偿算法。该算法采用简化Sage-Husa自适应滤波算法估计量测噪声,并通过协方差匹配技术引入收敛性判据抑制了滤波的发散,它提高了实时性且减少了计算量。实验结果表明:经过改进算法滤波后,MEMS陀螺随机振动误差的方差减少97.76%,与常规卡尔曼滤波相比,改进算法的方差减少了72.66%,验证了改进的自适应卡尔曼滤波算法可以有效地抑制MEMS陀螺因随机振动引起的输出误差。

ELECTRIC FIELD SENSORS BASED ON MEMS TECHNOLOGY

The design and optimization of two types of novel miniature vibrating Electric Field Sensors (EFSs) based on Micro Electro Mechanical Systems (MEMS) technology are presented.They have different structures and vibrating modes. The volume is much smaller than other types of charge-induced EFSs such as field-mills. As miniaturizing, the induced signal is reduced enormously and a high sensitive circuit is needed to detect it. Elaborately designed electrodes can increase the amplitude of the output current, making the detecting circuit simplified and improving the signal-to-noise ratio. Computer simulations for different structural parameters of the EFSs and vibrating methods have been carried out by Finite Element Method (FEM). It is proved that the new structures are realizable and the output signals are detectable.

基于双螺旋式加热器的柔性MEMS流量传感器

针对传统流量传感器的量程限制,提出了一种基于双螺旋结构加热器的柔性基底微机电系统(MEMS)热式流量传感器。在基底上打出均匀通孔阵列以降低热传导损耗,两对测温电阻对称分布在加热器两侧,以获得较宽量程,在微小流量与大流量下均表现出较高灵敏度。采用基于惠斯通电桥的检测电路实现输出电压的测量,同时保持加热器与环境温度恒定温差200 K,对电阻自热进行补偿。传感器工作温度-100~400℃,量程为0~60 m/s;低流速下灵敏度约为12.75 V/(m·s^(-1)),分辨率可达0.001 mm/s,高流速下灵敏度约为1.4 mV/(m·s^(-1)),功耗低至1.3 mW。

基于MEMS加速度计与磁力计的位移计设计

基坑水平位移监测是基坑安全的重要检测项目之一。针对现有的阵列式位移计在基坑水平位移监测中容易产生扭转误差的问题,设计了一种基于MEMS加速度计与磁力计的位移计,能够同时满足基坑水平位移与沉降位移监测的需求。介绍了位移计自动化实时监测系统组成。对位移计的结构以及监测节的硬件电路进行了设计,并制作完成了位移计样机。阐述了位移计通过对加速度计与磁力计的数据进行融合,计算位移坐标的测量原理和扭转误差校正的方法。最后通过模拟监测节间发生扭转时位移计水平位移监测的实验,以及位移计沉降位移监测试验。实验结果表明,该位移计能够精确测量水平与沉降位移,并能够有效校正位移计竖直安装时监测节扭转带来的扭转误差。

A Two-Scale Multi-Physics Deep Learning Model for Smart MEMS Sensors

Smart materials and structures, especially those bio-inspired, are often characterized by a hierarchy of length- and time-scales. Smart Micro Electro-Mechanical Systems (MEMS) are also characterized by different physical phenomena affecting their properties at different scales. Data-driven formulations can then be helpful to deal with the complexity of the multi-physics governing their response to the external stimuli, and optimize their performances. As an example, Lorentz force micro-magnetometers working principle rests on the interaction of a magnetic field with a current flowing inside a semiconducting, micro-structured medium. If an alternating current with a properly set frequency is let to flow longitudinally in a slender beam, the system is driven into resonance and the sensitivity to the magnetic field may result largely enhanced. In our former activity, a reduced-order physical model of the movable structure of a single-axis Lorentz force MEMS magnetometer was developed, to feed a multi-objective topology optimization procedure. That model-based approach did not account for stochastic effects, which lead to the scattering in the experimental data at the micrometric length-scale. The formulation is here improved to allow for stochastic effects through a two-scale deep learning model designed as follows: at the material scale, a neural network is adopted to learn the scattering in the mechanical properties of polysilicon induced by its polycrystalline morphology;at the device scale, a further neural network is adopted to learn the most important geometric features of the movable parts that affect the overall performance of the magnetometer. Some preliminary results are discussed, and an extension to allow for size effects is finally foreseen.

Frequency Features Based Fuzzy System for Rotating Machinery Vibration Analysis Using Smartphones Low-Cost MEMS Sensors

Smart devices have become an important entity for many applications in daily life activities. These devices have witnessed a rapid improvement in its technology to fulfill the increasingly diverse usage demands. In the meanwhile, rotating machinery vibration analysis based on low-cost sensors has gained a considerable attraction over the last few years. For a long time, the vibration analysis of machines has been accepted as an effective solution to detect and prevent failures in complex systems to avoid the sudden malfunction. The objective of this work is to use MEMS accelerometer measurements to monitor the different level of vibration of a machine. This work presents a new technique for rotating machinery vibration analysis. It uses Fast Fourier Transformation as a feature extraction algorithm and Fuzzy Logic System (FLS) as the classifier algorithm. A smartphone accelerometer is used to collect the data from the vibrating machine. The performance of the proposed technique is tested using data from different vibration resources at a different speed of operations. The results are discussed to illustrate the various vibration levels.

A Chopper Negative-R Delta-Sigma ADC for Audio MEMS Sensors

This paper presents a proposed low-noise and high-sensitivity Internet of Thing(IoT)system based on an M&NEMS microphone.The IoT device consists of an M&NEMS resistive accelerometer associated with an electronic readout circuit,which is a silicon nanowire and a Continuous-Time(CT)△∑ADC.The first integrator of the△∑ADC is based on a positive feedback DC-gain enhancement two-stage amplifier due to its high linearity and low-noise operations.To mitigate both the offset and 1/f noise,a suggested delay-time chopper negative-R stabilization technique is applied around the first integrator.A 65-nm CMOS process implements the CT△∑ADC.The supply voltage of the CMOS circuit is 1.2-V while 0.96-mW is the power consumption and 0.1-mm^(2) is the silicon area.The M&NEMS microphone and△∑ADC complete circuit are fabricated and measured.Over a working frequency bandwidth of 20-kHz,the measurement results of the proposed IoT system reach a signal to noise ratio(SNR)of 102.8-dB.Moreover,it has a measured dynamic range(DR)of 108-dB and a measured signal to noise and distortion ratio(SNDR)of 101.3-dB.

MEMS陀螺工作原理及其性能提升方法

MEMS陀螺是一种应用广泛的新型微惯性传感器,可实现高精度、小体积、抗干扰性强的角速度测量,被广泛应用于航空航天、平台钻井、自动驾驶、可穿戴设备以及手机中。近年来,随着对MEMS陀螺研究的不断深入,许多新型MEMS陀螺的工作原理被提出,由于测量原理的不同,其性能差异往往很大。本文旨在解决对MEMS陀螺的工作模式缺少系统性综述的问题,根据陀螺最终的读出技术,将MEMS陀螺的工作模式分为调幅、全角以及调频模式。在系统介绍其工作原理的基础上,详细讨论了各种工作模式下MEMS陀螺的性能提升方法。最后还介绍了各类MEMS陀螺的发展趋势,并结合各模式特性对MEMS陀螺的发展做出了展望。

一种基于硅基MEMS三维异构集成技术的T/R组件

基于硅基微电子机械系统(MEMS)三维异构集成工艺,设计并制作了用于相控阵天线系统的三维堆叠式Ku波段双通道T/R组件。该组件由两层硅基结构通过球栅阵列(BGA)植球堆叠而成,上下两层硅基封装均采用5层硅片通过硅通孔(TSV)、晶圆级键合工艺实现。组件集成了六位数控移相、六位数控衰减、串转并、电源调制、逻辑控制等功能,最终组件尺寸仅为15 mm×8 mm×3.8 mm。测试结果表明,在Ku波段内,该组件发射通道饱和输出功率大于24 dBm,单通道发射增益大于20 dB,接收通道增益大于20 dB,噪声系数小于3.0 dB。该组件性能好,质量轻,体积小,加工精确度高,组装效率高。