Trends in MEMS and Sensors
MEMS传感器的现状及发展前景
M E M S传感器的现状及发展前景集团标准化小组：[VVOPPT-JOPP28-JPPTL98-LOPPNN]毕业设计指导课论文MEMS传感器的现状及发展前景摘要：MEMS传感器是随着纳米技术的发展而兴起的新型传感器,具有很多新的特性,相对传统传感器其具有更大的优势。
在追求微型化的当代,其具有良好的发展前景,必将受到各个国家越来越多的重视。
文章首先介绍了MEMS传感器的分类和典型应用,然后着重对几个传感器进行了介绍,最后对MEMS传感器的发展趋势与发展前景进行了分析。
关键词：MEMS传感器;加度计;陀螺仪;纳米技术;微机构;微传感器StatusandDevelopmentProspectofMEMSSensorsAbstract:MEMSsensorisanewtypeofsensorwiththedevelopmentofnanotechnology.Ithasma nynewfeatures,whichhasagreatadvantageovertraditionalsensors.Inthepursuitofminia turizationofthecontemporary,itsgoodprospectsfordevelopment,willbesubjecttomoreandmoreattentioninvariouscountries.Firstly,theclassificationandtypicalapplicatio nofMEMSsensorareintroduced.Then,severalsensorsareintroduced.Finally,thedevelopm enttrendanddevelopmentprospectofMEMSsensorareanalyzed.Keywords:MEMSsensor;accelerometer;gyroscope;nanotechnology;micro-mechanism;micro-sensor目录一、引言MEMS传感器是采用微机械加工技术制造的新型传感器,是MEMS器件的一个重要分支。
微机电系统文献综述
基于Galerkin法分析微梁的动态响应一、课题研究背景1.MEMS的概念MEMS是微机电系统(Micro-Electro-Mechanical System)的英文缩写，是指将微结构的传感技术、致动技术和微电子控制技术集成于一体，形成同时具有“传感－计算(控制)－执行”功能的智能微型装置或微型系统[1]。
随着技术的兴起和发展，MEMS已成为继微电子技术之后在微尺度研究领域中的又一次革命。
MEMS通过力、电、磁等能量的转换来实现自身的特有功能，涉及多种物理场的互相耦合，因此它是一个多能量域耦合作用的极其复杂的系统。
2.MEMS的特点一般地说MEMS具有以下几个非约束性的特征：（1）MEMS器件体积小、重量轻、耗能低、惯性小、谐振频率高、响应时间短。
尺寸在毫米到微米范围之内，区别于一般宏(Macro)，即传统的、大于1cm 尺度的“机械”，并非进入物理上的微观层次。
（2）以硅为主要材料，机械电器性能优良：硅的强度、硬度和杨氏模量与铁相当，密度类似于铝，热传导率接近钼和钨。
基于(但不限于)硅微加工技术制造。
（3）批量生产大大降低了MEMS 产品成本。
用硅微加工工艺在一片硅片上同时可制造出成百上千个微型机电装置或完整的MEMS，批量生产使性能价格比比之传统“机械”制造技术大幅度地提高。
（4）集成化。
可以把不同功能、不同敏感方向的多个传感器或执行器集成于一体，或形成微传感器阵列、微执行器阵列，甚至把多种功能器件集成在一起，形成复杂的微系统。
微传感器、微执行器和微电子器件集成在一起可制造成可靠性、稳定性很高的MEMS。
3.MEMS的研究领域作为一门交叉学科，MEMS的研究和开发更是为了在微观领域探索新原理、开发新功能、制造新器件。
由于MEMS具有体系小、重量轻、能耗低、集成度高和智能化程度高等一系列优点，MEMS的研究领域不仅与微电子学密切相关，而且还广泛涉及到机械、材料、光学、流体、化学、热学、声学、磁学、自动控制、仿真学等学科，技术影响遍及包括各种传感器件、医疗、生物芯片、通信、机器人、能源、武器、航空航天等领域[2-5]，所以MEMS技术是一门多学科的综合技术。
MEMS传感器现状及应用
MEMS传感器现状及应用王淑华(中国电子科技集团公司第十三研究所,石家庄050051)摘要:M EM S传感器种类繁多,发展迅猛,应用广泛。
首先,简单介绍了M EMS传感器的分类和典型应用。
其次,对M EM S压力传感器、加速度计和陀螺仪三种最典型的MEM S传感器进行了详细阐述,包括类别、技术现状和性能指标、最新研究进展、产品,及应用情况。
介绍MEM S压力传感器时,给出了国内外采用新型材料制作用于极端环境下压力传感器的研究情况。
最后,从新材料、加工和组装技术方面对MEM S传感器的发展趋势进行了展望。
关键词:微电子机械系统(M EM S);传感器;加速度计;陀螺仪;压力传感器中图分类号:TH703文献标识码:A文章编号:1671-4776(2011)08-0516-07Current Status and Applications of MEMS SensorsWang Shuhua(T he13th Resear ch I ns titute,CE T C,S hij iaz huang050051,China)Abstract:MEMS sensors feature great varieties,rapid development and w ide applications.Firstly, the catego ries and ty pical applicatio ns of M EM S sensor s are introduced briefly.T hen three typ-i cal M EMS sensors,i1e.the pressure sensor,acceler ometer and g yrosco pe ar e illustrated in de-tail,including the subdiv ision,current technical capability and perfo rmance index,latest resear ch pro gress,products and their applications.Besides that,the research status of the MEM S pr es-sur e sensor using new m aterials for the extreme enviro nm ent at ho me and abro ad is presented. Finally,developm ent trends of M EM S sensors ar e predicted in term s o f new materials,pro ces-sing and assembling technolog y.Key words:micr oelectr omechanical system(M EM S);sensor;accelerom eter;gyr oscope;pr es-sur e sensorDOI:10.3969/j.issn.1671-4776.2011.08.008EEACC:25750引言MEM S传感器是采用微机械加工技术制造的新型传感器,是M EMS器件的一个重要分支。
MEMS Industry Status & Trends
Future
Consumer and Medical dominance * IDMs + Outsourcing by IDMs * Specialty MEMS Foundries (Low volume, high value products) * CMOS Foundries (Low cost, high volume products)
Manufacturing
Wafer size
* 8” dominance * 12”emergence
Processes/Packages with Reusable IP and building blocks for multiple products
Customization
Technology
© 2012 •
3-10 years High
2-4 years Low
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2011-2020 MEMS Market Forecast in $M
MF PS
MF IS
IS PS
© 2012 •
8
2011-2020 MEMS Market Forecast in units
RF
IS
MP
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© 2012 •
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MEMS Market Value Forecast Evolution
国内外电子信息工程领域的智能传感器研究综述
国内外电子信息工程领域的智能传感器研究综述摘要：智能传感器是电子信息工程领域的重要研究方向之一，其能够感知和获取环境中的各种信息，并通过内部处理和通信技术进行数据的处理和传输。
本综述通过对国内外智能传感器研究领域的文献梳理和总结，对智能传感器的分类、研究进展、应用场景进行了综合介绍，并对未来研究趋势进行了展望。
一、智能传感器的分类根据测量参数的不同，智能传感器可以分为温度传感器、压力传感器、湿度传感器、光传感器、加速度传感器、化学传感器等。
同时，根据智能传感器的工作原理，还可以将其分为电阻式传感器、电容式传感器、电感式传感器等。
二、国内外电子信息工程领域智能传感器的研究进展1. 传感器设计和制造技术：包括材料选择、传感元件设计、封装技术等。
2. 传感器信号处理技术：包括模拟信号处理技术、数字信号处理技术、嵌入式系统设计等。
3. 传感器通信技术：包括蓝牙、WiFi、LoRa、NB-IoT等无线通信技术的应用。
4. 传感器能耗优化技术：包括低功耗设计、能源收集技术、节能算法设计等。
三、智能传感器的应用场景1. 工业领域：智能传感器在工业自动化中的应用具有广泛的前景，可以实现对生产过程的监测和控制。
2. 农业领域：智能传感器在农业生产中的应用可以帮助农民进行精确的灌溉、施肥和植物生长环境监测等。
3. 城市建设和智能交通：智能传感器在城市交通监控、智能停车、交通信号优化等方面具有重要应用。
4. 医疗健康领域：智能传感器在医疗健康领域可以用于实时监测身体健康状况、药物释放等方面。
5. 环境监测与控制：智能传感器可以用于空气质量监测、水质监测、垃圾处理、环境保护等方面。
四、未来研究趋势展望1. 多模态传感器：通过整合多种不同类型的传感器，实现多样化数据的获取和处理。
2. 人工智能与智能传感器的结合：利用深度学习、机器学习等算法，提高传感器的自学习和自适应能力。
3. 高可靠性与能源自主：研究如何通过新材料和能量收集技术来提高传感器的可靠性和能源自主性。
MEMS惯导-单目视觉里程计组合导航技术研究
MEMS惯导-单目视觉里程计组合导航技术研究MEMS惯导/单目视觉里程计组合导航技术研究摘要：本文主要研究了MEMS惯导和单目视觉里程计组合导航技术。
MEMS惯导是一种高精度、低成本的惯性导航技术，而单目视觉里程计是一种基于相机视觉的位姿估计技术。
MEMS惯导和单目视觉里程计的组合可以互补各自的优点，提高导航的精度和鲁棒性。
首先，本文对MEMS惯导和单目视觉里程计的原理和特点进行了介绍，并对其存在的问题进行了分析。
然后，本文提出了一种基于卡尔曼滤波器的MEMS惯导/单目视觉里程计组合导航算法。
该算法将MEMS惯导和单目视觉里程计的位姿估计结果进行融合，得到更加准确和可靠的导航结果。
最后，本文进行了实验验证，结果表明，该算法在多种复杂环境下均能取得较好的导航精度和鲁棒性。
关键词：MEMS惯导；单目视觉里程计；组合导航；卡尔曼滤波器；导航精度；鲁棒性。
Abstract:This paper mainly studies the MEMS inertial navigation and monocular visual odometry combined navigation technology. MEMS inertial navigation is a high-precision and low-cost inertial navigation technology, while monocular visual odometry is a position and attitude estimation technology based on camera vision. The combination of MEMS inertial navigation and monocular visual odometry can complement each other's advantages and improve the accuracy and robustness of navigation.Firstly, this paper introduces the principles and characteristics of MEMS inertial navigation and monocular visual odometry, and analyzes the problems existing in them. Then, this paper proposes a MEMS inertial navigation/monocular visual odometry combined navigation algorithm based on Kalman filter. The algorithm fuses the position and attitude estimation results of MEMS inertial navigation and monocular visual odometry to obtain more accurate and reliable navigation results. Finally, this paper conducts experimental verification, and the results show that the algorithm can achieve good navigation accuracy and robustness in various complex environments.Keywords: MEMS inertial navigation; monocular visualodometry; combined navigation; Kalman filter; navigation accuracy; robustnessIn recent years, MEMS inertial navigation and monocular visual odometry have become popular among researchers as they provide accurate and low-cost navigation solutions. However, each approach has its limitations. MEMS inertial navigation suffers fromdrift errors, while monocular visual odometry is susceptible to lighting changes, occlusions, andmotion blur. To overcome these limitations,researchers have proposed a combined navigation approach that fuses the results of the two methods.One such approach is the Kalman filter-based algorithm, which integrates the measurements from MEMS inertial sensors and monocular vision to estimate the position and attitude of the system. The algorithm caneffectively suppress the drift errors of the inertial navigation system using the visual measurements as a reference, while compensating for the scale drifterror of the monocular visual odometry using theinertial measurements. Additionally, the algorithm can handle the nonlinearities and uncertainties of the navigation system and provide a more accurate and reliable navigation solution.To verify the effectiveness of the proposed algorithm, experimental tests were conducted in various complex environments. These tests included indoor and outdoor environments with different lighting conditions, as well as environments with obstacles and sudden movements. The results showed that the algorithm could achieve good navigation accuracy and robustness even in these challenging conditions.In conclusion, the combination of MEMS inertial navigation and monocular visual odometry using a Kalman filter-based algorithm is a promising approach to provide accurate and reliable navigation solutions. The algorithm can effectively address the limitations of both methods and is suitable for various complex environments. Future research should explore the application of this approach in specific fields, such as autonomous driving and robotics, to further evaluate its potentialOne potential application of this approach is in the field of autonomous driving. With the increasing demand for self-driving cars, accurate navigation becomes crucial for ensuring the safety and efficiency of the vehicle. By combining MEMS inertial navigation and monocular visual odometry, the proposed algorithm can provide precise location and orientationinformation for the autonomous vehicle. With the help of the Kalman filter, the algorithm can effectively correct errors and improve the overall accuracy of the navigation system.Another potential application is in the field of robotics. Many robotic systems require accurate positioning and orientation information to perform tasks such as mapping, exploration, and manipulation. By using the proposed approach, robotic systems can achieve higher precision and reliability in navigation, leading to improved performance and efficiency.However, there are still some challenges that need to be addressed. For example, the accuracy of the visual odometry system can be affected by external factors such as lighting conditions and camera calibration. The MEMS IMU system can also suffer from drift due to the accumulation of errors over time. To overcomethese challenges, researchers can explore the use of advanced sensor fusion techniques and machine learning algorithms.In summary, the combination of MEMS inertialnavigation and monocular visual odometry using a Kalman filter-based algorithm holds great potentialfor providing accurate and reliable navigationsolutions in various applications. Further researchand development in this area are needed to address the challenges and fully exploit the benefits of this approachOne area where the combination of MEMS inertial navigation and monocular visual odometry could prove particularly valuable is in autonomous vehicles.Autonomous vehicles rely on accurate and reliable navigation to operate safely and efficiently. While GPS is the primary navigation system used today, ithas limitations, such as poor performance in urban environments and susceptibility to jamming or spoofing.MEMS inertial navigation and monocular visual odometry offer an alternative or complementary approach to GPS-based navigation for autonomous vehicles. By using highly accurate inertial sensors and cameras to measure vehicle motion and track landmarks, these systems can provide precise and reliable position and orientation information.One of the key advantages of using these technologiesin combination is their redundancy. MEMS inertial navigation can provide accurate position andorientation estimates over short periods of time, buterrors can accumulate over longer periods due to drift. Monocular visual odometry can help correct theseerrors by providing additional position andorientation estimates based on image data.However, using these technologies in an autonomous vehicle setting presents several challenges. For example, the vehicle may encounter scenarios where the camera cannot see sufficient landmarks to track its position accurately. Additionally, environmentalfactors such as lighting conditions and weather can also affect the performance of visual odometry.To overcome these challenges, advanced algorithms and sensor fusion techniques, such as deep learning and Kalman filtering, can be used to optimize the performance of the system. For example, a deeplearning-based object recognition algorithm could be trained to identify and track specific landmarks that are more robust to changes in environmental conditions.Another potential application for MEMS inertial navigation and visual odometry is in robotics. For example, in warehouse automation, robots that can navigate accurately and efficiently can help improve the speed and productivity of operations whilereducing costs.Overall, the combination of MEMS inertial navigation and monocular visual odometry has significantpotential for a wide range of applications. Continued research and development in this area will be critical to realizing the full benefits of these technologies in practical settingsIn conclusion, MEMS inertial navigation and monocular visual odometry are powerful technologies that can be used together for various applications, such as autonomous vehicles, drones, virtual reality, and robotics. They can improve accuracy, reliability, and efficiency while reducing costs. Continued research and development in this area is essential to fully unlock the potential of these technologies inpractical settings。
测控技术与仪器专业英语unit
全句译为：然而，供应电压从5V衰减到3.3V甚至更低， 以及系统中多种电压形式的出现，并不只是对最智能的传 感器提出的考验。
Separate integrated circuits (ICs) are available to handle the variety of voltages and resolve the problem, but they add to system and sensor complexity. 译为：单独的集成电路（ICs）可用来处理各种不同的电压 并解决问题，但它们增加了系统和传感器的复杂性。
译为：该转换器将测量的物理量进行转换。观察员对系 统进行修正以使结果接近理想值。典型的测量系统的组成 框图如图3.1所示。
Figure 3.1 General sensing system
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Unit3 Smart Sensors
Many home thermostats（恒温（调节）器）, tire pressure gauges（轮胎气压表）, and factory flow meters still operate in the same manner.
Unit3 Smart Sensors
1
Unit3 Smart Sensors
Introduction
Just about everything today in the technology area is a candidate（申请求职者，候选人；报考者）for having the prefix （前缀）smart added to it. The term smart sensor was coined in the mid-1980s, and since then several devices have been called smart sensors.
传感器国内外发展现状
传感器国内外发展现状传感器是现代科技中非常重要的一个组成部分，它们广泛应用于各个领域，包括工业制造、医疗保健、智能家居、物联网等。
然而，由于各种原因，国内外在传感器技术发展方面存在一定的差距。
国外传感器技术的发展相对较早，尤其是发达国家如美国、德国等。
这些国家有着强大的科研实力和创新能力，不断推动着传感器技术向前发展。
目前，国外在传感器技术的研究和应用上具有一定的优势。
首先，在传感器技术方面，国外已经研发出许多先进的传感器产品。
这些产品具有高精度、高灵敏度和高可靠性的特点。
比如，气体传感器可以实现对环境中各种有害气体的检测和监测；压力传感器可以测量和控制各种气体和液体中的压力变化；温度传感器能够精确测量温度值等等。
此外，国外还研发出了许多新型的传感器技术，如光纤传感器、生物传感器、MEMS传感器等，这些传感器在不同领域有着广泛的应用。
其次，在传感器应用领域方面，国外的发展也比较成熟。
工业制造、汽车行业、医疗保健领域是传感器应用的主要领域。
国外的制造业在传感器技术的应用上更加广泛，能够精确地监测和控制生产过程中的各种参数变化。
汽车行业则广泛应用各种传感器来提高安全性和驾驶体验。
医疗保健领域也借助传感器技术来监测患者的健康状况。
但是，国内在传感器技术的发展上也有一定的突破。
近年来，我国政府加大了对科技创新的支持力度，鼓励企业和科研机构加大对传感器技术的研发投入。
国内一些企业也开始在传感器领域进行技术创新，并取得了一些成果。
例如，一些高校和科研机构在MEMS传感器、光纤传感器等领域取得了较好的研究成果。
同时，国内的制造业、医疗保健、智能家居等领域也开始广泛应用传感器技术。
综上所述，国内外在传感器技术发展方面存在一定的差距。
国外在传感器技术的研发和应用上具有较大的优势，但国内也在积极迎头赶上，通过政府的支持和企业的努力，国内传感器技术的发展正在逐步加快。
未来，传感器技术的发展将有望推动各个领域的创新与进步。
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MEMS & Sensors Industry Group® | All Rights Reserved
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Conclusion
MEMS & Sensors is a growing, exciting industry but there’s a lot of work to be done to enable IoT
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Source: IHS Markit – MEMS Competitive Landscape Analysis 2016
The Top 30 earn ~80% of industry revenue
Who’s Making the $$ in MEMS & Sensors?
• Smart health from wearables to disease detection (cancer) • Smart automotive: automated driving to autonomous vehicles • Smart homes: smart meters to fully automated, self-sustaining homes and communities (power grid coordination within ecosystem) • Smart cities: smart parking, buildings, streets, garbage…
IoT is Happening, Now
IoT is REAL (not just hype) and expected to grow to $11T-$19T by 2025 (McKinsey and Cisco, respectively) IoT is enabled by MEMS and sensors - Forecasts for sensor demand as high as 100T by 2030 (Janusz Bryzek) Breakthrough opportunities when integrated with the applications ecosystem (holistically with hardware, software, analytics) IoT is requiring unprecedented scaling of data analytics, artificial intelligence/machine learning In this competitive environment, MEMS and sensor companies need to focus on value, creating smart systems and end-to-end solutions; while still addressing remaining tech challenges
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Billions of US$
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Wired communications Military & Civil Aerospace Medical Electronics Industry Data processing Automotive Consumer and Mobile
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2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
VALUE MEMS and sensor companies need to focus on value, creating
smart systems and end-to-end solutions
MEMS & Sensors Industry Group® | All Rights Reserved
COOPERATIVE SYSTEMS
Database & infrastructure supported
COLLABORATIVE SYSTEMS
Dynamic self-controlled systems. Configuration is function of capabilities of the nodes
Trends in MEMS & Sensors
IoT = Key Market Driver for MEMS & Sensors
IoT
Courtesy of Bosch Sensortec
IoT poised to be a big market driver for MEMS & sensors Specifically the ecosystem supporting “smart” everything, for example:
MEMS & Sensors Industry Group® | All Rights Reserved
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MEMS Market by Application
Double digit CAGR for CE/Mobile, Medical and Wired Communications
Total MEMS Market by Applications
5
Bosch Avago Texas Instruments HP STMicroelectronics Qorvo (TriQuint) Knowles InvenSense Canon NXP Sensata Analog Devices Denso Epson Panasonic Murata FormFactor Infineon Goertek AAC Micronics Japan Co Honeywell Amphenol AKM FLIR Delphi TDK-EPC (Epcos) Excelitas (former Perkin… Rohm (Kionix) First Sensor ULIS MEMSIC Megachip (SiTime) Raytheon TE Connectivity Silicon Sensing Systems Polatis BSE DRS Alps Meggitt (Endevco) Funai (former Lexmark) Sensirion Omron Fuji Electric ams (AppliedSensor) Fujifilm-Dimatix Phicom Melexis NeoMEMS
10
Revenue of top IDM and Fabless MEMS manufacturers in 2015
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© 2016 IHS
We still have a LONG way to go to get to IoT/E
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6
Challenges and Opportunities
Hyper-connected world pushes requirements in system reliability
RELIABILITY & DEPENDABILITY
Source: IHS Markit – MEMS Market Tracker H1 2016 © 2016 IHS
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3
Where is the Value/ROI?
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POWER
Power management and creation (energy harvesting) still remain relatively unsolved challenges
SECURITY
Security (separate from privacy) is critical at the sensor node level to ensure secure end-to-end solutions
NETWORKING LEVEL
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SUPPORTIVE SYSTEMS
DEPENDABLE SYSTEMS
MIChallenges
STANDARDS
Standards needed for more interoperability between devices to enable smart cities and IoT