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Open AccessArticle

Built-In Packaging for Single Terminal Devices

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Eihab M. Abdel-Rahman

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Mustafa Yavuz

Sensors 2022, 22(14), 5264; https://doi.org/10.3390/s22145264 - 14 Jul 2022

Viewed by 984

Abstract

An alternative packaging method, termed built-in packaging, is proposed for single terminal devices, and demonstrated with an actuator application. Built-in packaging removes the requirements of wire bonding, chip carrier, PCB, probe station, interconnection elements, and even wires to drive single terminal devices. Reducing [...] Read more.

An alternative packaging method, termed built-in packaging, is proposed for single terminal devices, and demonstrated with an actuator application. Built-in packaging removes the requirements of wire bonding, chip carrier, PCB, probe station, interconnection elements, and even wires to drive single terminal devices. Reducing these needs simplifies operation and eliminates possible noise sources. A micro resonator device is fabricated and built-in packaged for demonstration with electrostatic actuation and optical measurement. Identical actuation performances are achieved with the most conventional packaging method, wire bonding. The proposed method offers a compact and cheap packaging for industrial and academic applications. Full article

(This article belongs to the Special Issue MEMS and NEMS Sensors)

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Open AccessArticle

Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN

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Sensors 2022, 22(14), 5225; https://doi.org/10.3390/s22145225 - 13 Jul 2022

Viewed by 783

Abstract

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the [...] Read more.

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s² to −1.2646 × 10⁻⁷ m/s², while the variance decreases from 0.0133 m/s² to 1.0975 × 10⁻⁵ m/s². In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals. Full article

(This article belongs to the Special Issue MEMS and NEMS Sensors)

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Open AccessArticle

Compact Sphere-Shaped Airflow Vector Sensor Based on MEMS Differential Pressure Sensors

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Sensors 2022, 22(3), 1087; https://doi.org/10.3390/s22031087 - 30 Jan 2022

Cited by 2 | Viewed by 2081

Abstract

This paper presents an airflow vector sensor for drones. Drones are expected to play a role in various industrial fields. However, the further improvement of flight stability is a significant issue. In particular, compact drones are more affected by wind during flight. Thus, [...] Read more.

This paper presents an airflow vector sensor for drones. Drones are expected to play a role in various industrial fields. However, the further improvement of flight stability is a significant issue. In particular, compact drones are more affected by wind during flight. Thus, it is desirable to detect air current directly by an airflow sensor and feedback to the control. In the case of a drone in flight, the sensor should detect wind velocity and direction, particularly in the horizontal direction, for a sudden crosswind. In addition, the sensor must also be small, light, and highly sensitive. Here, we propose a compact spherical airflow sensor for drones. Three highly sensitive microelectromechanical system (MEMS) differential pressure (DP) sensor chips were built in the spherical housing as the sensor elements. The 2D wind direction and velocity can be measured from these sensor elements. The fabricated airflow sensor was attached to a small toy drone. It was demonstrated that the sensor provided an output corresponding to the wind velocity and direction when horizontal wind was applied via a fan while the drone was flying. The experimental results demonstrate that the proposed sensor will be helpful for directly measuring the air current for a drone in flight. Full article

(This article belongs to the Special Issue MEMS and NEMS Sensors)

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Open AccessArticle

Inertial Motion Capture-Based Whole-Body Inverse Dynamics

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Sensors 2021, 21(21), 7353; https://doi.org/10.3390/s21217353 - 05 Nov 2021

Cited by 7 | Viewed by 1608

Abstract

Inertial Motion Capture (IMC) systems enable in situ studies of human motion free of the severe constraints imposed by Optical Motion Capture systems. Inverse dynamics can use those motions to estimate forces and moments developing within muscles and joints. We developed an inverse [...] Read more.

Inertial Motion Capture (IMC) systems enable in situ studies of human motion free of the severe constraints imposed by Optical Motion Capture systems. Inverse dynamics can use those motions to estimate forces and moments developing within muscles and joints. We developed an inverse dynamic whole-body model that eliminates the usage of force plates (FPs) and uses motion patterns captured by an IMC system to predict the net forces and moments in 14 major joints. We validated the model by comparing its estimates of Ground Reaction Forces (GRFs) to the ground truth obtained from FPs and comparing predictions of the static model’s net joint moments to those predicted by 3D Static Strength Prediction Program (3DSSPP). The relative root-mean-square error (rRMSE) in the predicted GRF was 6% and the intraclass correlation of the peak values was 0.95, where both values were averaged over the subject population. The rRMSE of the differences between our model’s and 3DSSPP predictions of net L5/S1 and right and left shoulder joints moments were 9.5%, 3.3%, and 5.2%, respectively. We also compared the static and dynamic versions of the model and found that failing to account for body motions can underestimate net joint moments by 90% to 560% of the static estimates. Full article

(This article belongs to the Special Issue MEMS and NEMS Sensors)

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Open AccessCommunication

Characterization of Shear Horizontal Waves Using a 1D Laser Doppler Vibrometer

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Alaa Elhady

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Eihab M. Abdel-Rahman

Sensors 2021, 21(7), 2467; https://doi.org/10.3390/s21072467 - 02 Apr 2021

Cited by 3 | Viewed by 1129

Abstract

We developed a new technique for the detection of shear horizontal surface acoustic waves (SH-SAW) using a one-dimensional laser-based Doppler vibrometer. It measures the out-of-plane surface deformation at the fingertip of an interdigitated transducer (the boundary of the wave aperture) and uses it [...] Read more.

We developed a new technique for the detection of shear horizontal surface acoustic waves (SH-SAW) using a one-dimensional laser-based Doppler vibrometer. It measures the out-of-plane surface deformation at the fingertip of an interdigitated transducer (the boundary of the wave aperture) and uses it to estimate the instantaneous in-plane displacement field given the substrate Poisson ratio. It can also estimate the degree of surface confinement (wave decay rate). The proposed approach was first verified using finite element analysis (FEA) and demonstrated experimentally using a Bleustein–Gulyaev resonator. Full article

(This article belongs to the Special Issue MEMS and NEMS Sensors)

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Open AccessArticle

Defect Filling Method of Sensor Encapsulation Based on Micro-Nano Composite Structure with Parylene Coating

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Sensors 2021, 21(4), 1107; https://doi.org/10.3390/s21041107 - 05 Feb 2021

Cited by 4 | Viewed by 1818

Abstract

The demand for waterproofing of polymer (parylene) coating encapsulation has increased in a wide variety of applications, especially in the waterproof protection of electronic devices. However, parylene coatings often produce pinholes and cracks, which will reduce the waterproof effect as a protective barrier. [...] Read more.

The demand for waterproofing of polymer (parylene) coating encapsulation has increased in a wide variety of applications, especially in the waterproof protection of electronic devices. However, parylene coatings often produce pinholes and cracks, which will reduce the waterproof effect as a protective barrier. This characteristic has a more significant influence on sensors and actuators with movable parts. Thus, a defect filling method of micro-nano composite structure is proposed to improve the waterproof ability of parylene coatings. The defect filling method is composed of a nano layer of Al₂O₃ molecules and a micro layer of parylene polymer. Based on the diffusion mechanism of water molecules in the polymer membrane, defects on the surface of polymer encapsulation will be filled and decomposed into smaller areas by Al₂O₃ nanoparticles to delay or hinder the penetration of water molecules. Accordingly, the dense Al₂O₃ nanoparticles are utilized to fill and repair the surface of the organic polymer by low-rate atomic layer deposition. This paper takes the pressure sensor as an example to carry out the corresponding research. Experimental results show that the proposed method is very effective and the encapsulated sensors work properly in a saline solution after a period of time equivalent to 153.9 days in body temperature, maintaining their accuracy and precision of 2 mmHg. Moreover, the sensors could improve accuracy by about 43% after the proposed encapsulation. Therefore, the water molecule anti-permeability encapsulation would have broad application prospects in micro/nano-device protection. Full article

(This article belongs to the Special Issue MEMS and NEMS Sensors)

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Open AccessArticle

Design, Analysis and Simulation of a MEMS-Based Gyroscope with Differential Tunneling Magnetoresistance Sensing Structure

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Sensors 2020, 20(17), 4919; https://doi.org/10.3390/s20174919 - 31 Aug 2020

Cited by 8 | Viewed by 2517

Abstract

The design, analysis, and simulation of a new Micro-electromechanical System (MEMS) gyroscope based on differential tunneling magnetoresistance sensing are presented in this paper. The device is driven by electrostatic force, whereas the Coriolis displacements are transferred to intensity variations of magnetic fields, further [...] Read more.

The design, analysis, and simulation of a new Micro-electromechanical System (MEMS) gyroscope based on differential tunneling magnetoresistance sensing are presented in this paper. The device is driven by electrostatic force, whereas the Coriolis displacements are transferred to intensity variations of magnetic fields, further detected by the Tunneling Magnetoresistance units. The magnetic fields are generated by a pair of two-layer planar multi-turn copper coils that are coated on the backs of the inner masses. Together with the dual-mass structure of proposed tuning fork gyroscope, a two-stage differential detection is formed, thereby enabling rejection of mechanical and magnetic common-mode errors concurrently. The overall conception is described followed by detailed analyses of proposed micro-gyroscope and rectangle coil. Subsequently, the FEM simulations are implemented to determine the mechanical and magnetic characteristics of the device separately. The results demonstrate that the micro-gyroscope has a mechanical sensitivity of 1.754 nm/°/s, and the micro-coil has a maximum sensitivity of 41.38 mOe/µm. When the detection height of Tunneling Magnetoresistance unit is set as 60 µm, the proposed device exhibits a voltage-angular velocity sensitivity of 0.131 mV/°/s with a noise floor of 7.713 × 10⁻⁶°/s/

\sqrt{Hz}

in the absence of any external amplification. Full article

(This article belongs to the Special Issue MEMS and NEMS Sensors)

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Open AccessLetter

Characteristics Research of a High Sensitivity Piezoelectric MOSFET Acceleration Sensor

by

Chunpeng Ai

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Xiaofeng Zhao

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Dianzhong Wen

Sensors 2020, 20(17), 4988; https://doi.org/10.3390/s20174988 - 03 Sep 2020

Cited by 5 | Viewed by 2930

Abstract

In order to improve the output sensitivity of the piezoelectric acceleration sensor, this paper proposed a high sensitivity acceleration sensor based on a piezoelectric metal oxide semiconductor field effect transistor (MOSFET). It is constituted by a piezoelectric beam and an N-channel depletion MOSFET. [...] Read more.

In order to improve the output sensitivity of the piezoelectric acceleration sensor, this paper proposed a high sensitivity acceleration sensor based on a piezoelectric metal oxide semiconductor field effect transistor (MOSFET). It is constituted by a piezoelectric beam and an N-channel depletion MOSFET. A silicon cantilever beam with Pt/ZnO/Pt/Ti multilayer structure is used as a piezoelectric beam. Based on the piezoelectric effect, the piezoelectric beam generates charges when it is subjected to acceleration. Due to the large input impedance of the MOSFET, the charge generated by the piezoelectric beam can be used as a gate control signal to achieve the purpose of converting the output charge of the piezoelectric beam into current. The test results show that when the external excitation acceleration increases from 0.2 g to 1.5 g with an increment of 0.1 g, the peak-to-peak value of the output voltage of the proposed sensors increases from 0.327 V to 2.774 V at a frequency of 1075 Hz. The voltage sensitivity of the piezoelectric beam is 0.85 V/g and that of the proposed acceleration sensor was 2.05 V/g, which is 2.41 times higher than the piezoelectric beam. The proposed sensor can effectively improve the voltage output sensitivity and can be used in the field of structural health monitoring. Full article

(This article belongs to the Special Issue MEMS and NEMS Sensors)

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Open AccessLetter

Measurement of In-Plane Motions in MEMS

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Sensors 2020, 20(12), 3594; https://doi.org/10.3390/s20123594 - 25 Jun 2020

Cited by 3 | Viewed by 2294

Abstract

We report a technique to measure in-plane and out-of-plane motions of MEMS using typical out-of-plane (single-axis) Laser Doppler Vibrometers (LDVs). The efficacy of the technique is demonstrated by evaluating the in-plane and out-of-plane modal response and frequency response of an interdigitated comb-drive actuator. [...] Read more.

We report a technique to measure in-plane and out-of-plane motions of MEMS using typical out-of-plane (single-axis) Laser Doppler Vibrometers (LDVs). The efficacy of the technique is demonstrated by evaluating the in-plane and out-of-plane modal response and frequency response of an interdigitated comb-drive actuator. We also investigate the validity of observing planar modes of vibration outside their dominant plane of motion and find that it leads to erroneous results. Planar modes must be evaluated in their plan of motion. Full article

(This article belongs to the Special Issue MEMS and NEMS Sensors)

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