Patterned Polymer Bonding with 3-D capability for Microelectromechanical Systems (MEMS) Inertial Sensors

2006 ◽  
Author(s):  
E.J. Tuck ◽  
G. Tuck ◽  
M. Buncick ◽  
T. Hudson ◽  
T. Buckner
Keyword(s):  
Microelectromechanical Systems ◽  
Inertial Sensors

Tailoring of Stress Development in MEMS Packaging Systems

2002 ◽  
Vol 741 ◽  
Author(s):  
Satyajit S. Walwadkar ◽  
Junghyun Cho ◽  
P.W. Farrell ◽  
Lawrence E. Felton
Keyword(s):  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Thermal Exposure ◽  
Origin And Evolution ◽  
Mems Packaging ◽  
Operation Conditions ◽  
Die Attach ◽  
Device Operation ◽  
Different Levels ◽  
Good Agreement

ABSTRACTA better understanding of the origin and evolution of the stresses is a crucial step in improving reliability of packaging systems for microelectromechanical systems (MEMS). Given its importance, we examine the stresses developed in hermetically packaged MEMS inertial sensors. For this purpose, an optical surface profilometer is employed to assess the stresses by measuring the curvature of dummy silicon dies (3.5×3.5 mm2) assembled in different types of packages and die attach adhesives. We also explore a temporal evolution of stresses during thermal exposure of the test packages in an effort to emulate actual packaging processes and device operation conditions. The result shows different levels of stresses generated from various adhesives and package types, and also a stress evolution during packaging processes. The mechanical stress data also show a good agreement with MEMS performance data obtained from actual accelerometers. Therefore, the stress data will not only be useful in better understanding performance of MEMS packages, but the testing protocol can also provide a diagnostic tool for very small packaging systems.

scholarly journals Monolithic Multi Degree of Freedom (MDoF) Capacitive MEMS Accelerometers

Micromachines ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 602 ◽  
Author(s):  
Zakriya Mohammed ◽  
Ibrahim Elfadel ◽  
Mahmoud Rasras
Keyword(s):  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Electrical Contact ◽  
High Sensitivity ◽  
Low Noise ◽  
Degree Of Freedom ◽  
Noise Floor ◽  
Mems Accelerometers ◽  
Capacitive Mems ◽  
Cross Axis

With the continuous advancements in microelectromechanical systems (MEMS) fabrication technology, inertial sensors like accelerometers and gyroscopes can be designed and manufactured with smaller footprint and lower power consumption. In the literature, there are several reported accelerometer designs based on MEMS technology and utilizing various transductions like capacitive, piezoelectric, optical, thermal, among several others. In particular, capacitive accelerometers are the most popular and highly researched due to several advantages like high sensitivity, low noise, low temperature sensitivity, linearity, and small footprint. Accelerometers can be designed to sense acceleration in all the three directions (X, Y, and Z-axis). Single-axis accelerometers are the most common and are often integrated orthogonally and combined as multiple-degree-of-freedom (MDoF) packages for sensing acceleration in the three directions. This type of MDoF increases the overall device footprint and cost. It also causes calibration errors and may require expensive compensations. Another type of MDoF accelerometers is based on monolithic integration and is proving to be effective in solving the footprint and calibration problems. There are mainly two classes of such monolithic MDoF accelerometers, depending on the number of proof masses used. The first class uses multiple proof masses with the main advantage being zero calibration issues. The second class uses a single proof mass, which results in compact device with a reduced noise floor. The latter class, however, suffers from high cross-axis sensitivity. It also requires very innovative layout designs, owing to the complicated mechanical structures and electrical contact placement. The performance complications due to nonlinearity, post fabrication process, and readout electronics affects both classes of accelerometers. In order to effectively compare them, we have used metrics such as sensitivity per unit area and noise-area product. This paper is devoted to an in-depth review of monolithic multi-axis capacitive MEMS accelerometers, including a detailed analysis of recent advancements aimed at solving their problems such as size, noise floor, cross-axis sensitivity, and process aware modeling.

scholarly journals Upper Body Posture Recognition Using Inertial Sensors and Recurrent Neural Networks

2021 ◽  
Vol 11 (24) ◽  
pp. 12101
Author(s):  
Hao-Yuan Tang ◽  
Shih-Hua Tan ◽  
Ting-Yu Su ◽  
Chang-Jung Chiang ◽  
Hsiang-Ho Chen
Keyword(s):  
Microelectromechanical Systems ◽  
Short Term Memory ◽  
Inertial Sensors ◽  
Sagittal Plane ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Classification Model ◽  
Upper Body ◽  
Sitting Posture ◽  
Posture Recognition

Inadequate sitting posture can cause imbalanced loading on the spine and result in abnormal spinal pressure, which serves as the main risk factor contributing to irreversible and chronic spinal deformity. Therefore, sitting posture recognition is important for understanding people’s sitting behaviors and for correcting inadequate postures. Recently, wearable devices embedded with microelectromechanical systems (MEMs) sensors, such as inertial measurement units (IMUs), have received increased attention in human activity recognition. In this study, a wearable device embedded with IMUs and a machine learning algorithm were developed to classify seven static sitting postures: upright, slump, lean, right and left bending, and right and left twisting. Four 9-axis IMUs were uniformly distributed between thoracic and lumbar regions (T1-L5) and aligned on a sagittal plane to acquire kinematic information about subjects’ backs during static-dynamic alternating motions. Time-domain features served as inputs to a signal-based classification model that was developed using long short-term memory-based recurrent neural network (LSTM-RNN) architecture, and the model’s classification performance was used to evaluate the relevance between sensor signals and sitting postures. Overall results from performance evaluation tests indicate that this IMU-based measurement and LSTM-RNN structural scheme was appropriate for sitting posture recognition.

scholarly journals Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

Micromachines ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1021
Author(s):  
Shipeng Han ◽  
Zhen Meng ◽  
Olatunji Omisore ◽  
Toluwanimi Akinyemi ◽  
Yuepeng Yan
Keyword(s):  
Random Error ◽  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Random Noise ◽  
Development Trend ◽  
Sensor Technology ◽  
Application Range ◽  
Mems Accelerometers ◽  
Mems Gyroscopes

Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

scholarly journals A Systematic Review of Performance Analysis in Rowing Using Inertial Sensors

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1304 ◽  
Author(s):  
Worsey ◽  
Espinosa ◽  
Shepherd ◽  
Thiel
Keyword(s):  
Best Practice ◽  
Performance Monitoring ◽  
Ad Hoc ◽  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Magnetic Sensors ◽  
Sensor Technology ◽  
Future Research ◽  
Rowing Performance

Sporting organizations such as professional clubs and national sport institutions are constantly seeking novel training methodologies in an attempt to give their athletes a cutting edge. The advent of microelectromechanical systems (MEMS) has facilitated the integration of small, unobtrusive wearable inertial sensors into many coaches’ training regimes. There is an emerging trend to use inertial sensors for performance monitoring in rowing; however, the use and selection of the sensor used has not been appropriately reviewed. Previous literature assessed the sampling frequency, position, and fixing of the sensor; however, properties such as the sensor operating ranges, data processing algorithms, and validation technology are left unevaluated. To address this gap, a systematic literature review on rowing performance monitoring using inertial-magnetic sensors was conducted. A total of 36 records were included for review, demonstrating that inertial measurements were predominantly used for measuring stroke quality and the sensors were used to instrument equipment rather than the athlete. The methodology for both selecting and implementing technology appeared ad hoc, with no guidelines for appropriate analysis of the results. This review summarizes a framework of best practice for selecting and implementing inertial sensor technology for monitoring rowing performance. It is envisaged that this review will act as a guide for future research into applying technology to rowing.

Detection of Gait Event and Supporting Leg during Overground Walking with Mediolateral Swing Angle

2011 ◽  
Vol 145 ◽  
pp. 567-573 ◽  
Author(s):  
Je Nam Kim ◽  
Mun Ho Ryu ◽  
Yoon Seok Yang ◽  
Seong Hyun Kim
Keyword(s):  
Gait Analysis ◽  
Motion Tracking ◽  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Tracking System ◽  
Reference Method ◽  
Vertical Acceleration ◽  
Elastic Band ◽  
Swing Angle

Walking is one of the basic human activities. Several well-defined, motion tracking systems have been used for gait analysis. However, these systems such as the optical motion tracking system are very expensive and limited to laboratory usage. Recently, microelectromechanical systems (MEMS)-based inertial sensors have made it possible to overcome these disadvantages. The aim of this study was to identify gait events and the supporting leg by measuring the mediolateral swing angle. An inertial sensor unit with a 3-axis accelerometer and 2-axis gyroscope was attached to the subject’s lower trunk using an elastic band. Five, healthy and young (20–29 yrs.) subjects participated in this experiment. Each walked twice along a straight, 25-m path at three different speeds. During each trial, the sensor transmitted signals to a PC via Bluetooth technology. In this study, gait events and the supporting leg were identified using the peak and sign of the mediolateral swing angle. The mediolateral swing angle was calculated using the integrated gyroscope signal. For comparison, a well-defined spatiotemporal gait analysis technique was also applied. In this reference method, the gait event was identified with the last peak of the vertical acceleration before the sign change from positive to negative. The supporting leg was identified using the sign of the mediolateral acceleration double integration. Identification of the supporting leg was difficult in the reference method because of the offset and gravity components in the mediolateral acceleration. However, the proposed method reported here, showed stable identification of gait events and the supporting leg. This study could be expanded to more detailed gait analysis with the additional fusion of a 3-axis acceleration, gyroscope and magnetometer.

Integrated MEMS Technologies

MRS Bulletin ◽  
2001 ◽  
Vol 26 (4) ◽  
pp. 291-295 ◽  
Author(s):  
Andrea E. Franke ◽  
Tsu-Jae King ◽  
Roger T. Howe
Keyword(s):  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Mems Devices ◽  
Sensors And Actuators ◽  
Component Level ◽  
Substantial Impact ◽  
The Past ◽  
Current State ◽  
Mems Technology

While microelectromechanical systems (MEMS) technology has made a substantial impact over the past decade at the device or component level, it has yet to realize the “S” in its acronym, as complex microsystems consisting of sensors and actuators integrated with sense, control, and signal-processing electronics are still beyond the current state of the art. There are several incentives to co-fabricate MEMS devices and electronics on a single silicon chip, which apply to applications such as inertial sensors.

scholarly journals Peculiarities and Applications of Stochastic Processes with Fractal Properties

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5960
Author(s):  
Oleg Semenovich Amosov ◽  
Svetlana Gennadievna Amosova
Keyword(s):  
Stochastic Processes ◽  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Flaw Detection ◽  
Failure Detection ◽  
Self Similarity ◽  
Mobile Objects ◽  
Fractal Properties ◽  
Ultrasonic Echo ◽  
Non Destructive

In this paper, the fractal properties of stochastic processes and objects in different areas were specified and investigated. These included: measuring systems and sensors, navigation and motion controls, telecommunication systems and networks, and flaw detection technologies. Additional options that occur through the use of fractality were also indicated and exemplified for each application. Regarding the problems associated with navigation information processing, the following fractal nature processes were identified: errors of inertial sensors based on the microelectromechanical systems called MEMS, in particular gyroscopic drift and accelerometer bias, and; the trajectory movement of mobile objects. With regard to navigation problems specifically, the estimation problem statement and its solution are given by way of the Bayesian approach for processing fractal processes. The modified index of self-similarity for telecommunication series was proposed, and the self-similarity of network traffic based on the R/S method and wavelet analysis was identified. In failure detection, fractality manifested as porosity, wrinkles, surface fractures, and ultrasonic echo signals measured using non-destructive sensors used for rivet compound testing.

scholarly journals GNSS/Low-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Hassana Maigary Georges ◽  
Dong Wang ◽  
Zhu Xiao
Keyword(s):  
Urban Areas ◽  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Low Cost ◽  
Robust Solution ◽  
Positioning Systems ◽  
Variational Bayesian ◽  
Kalman Smoother ◽  
Model Combining ◽  
Artificial Neural Network Ann

Among the inertial navigation system (INS) devices used in land vehicle navigation (LVN), low-cost microelectromechanical systems (MEMS) inertial sensors have received more interest for bridging global navigation satellites systems (GNSS) signal failures because of their price and portability. Kalman filter (KF) based GNSS/INS integration has been widely used to provide a robust solution to the navigation. However, its prediction model cannot give satisfactory results in the presence of colored and variational noise. In order to achieve reliable and accurate positional solution for LVN in urban areas surrounded by skyscrapers or under dense foliage and tunnels, a novel model combining variational Bayesian adaptive Kalman smoother (VB-ACKS) as an alternative of KF and ensemble regularized extreme learning machine (ERELM) for bridging global positioning systems outages is proposed. The ERELM is applied to reduce the fluctuating performance of GNSS during an outage. We show that a well-organized collection of predictors using ensemble learning yields a more accurate positional result when compared with conventional artificial neural network (ANN) predictors. Experimental results show that the performance of VB-ACKS is more robust compared with KF solution, and the prediction of ERELM contains the smallest error compared with other ANN solutions.

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