A microelectromechanical system (MEMS) capacitive accelerometer-based microphone with enhanced sensitivity for fully implantable hearing aid: a novel analytical approach
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Apoorva Dwivedi
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Gargi Khanna
Abstract
The present work proposes a novel, compact, intuitively simple and efficient structure to improve the sensitivity of a microelectromechanical system (MEMS) capacitive accelerometer using an arrangement of microlever as a displacement amplifier. The accelerometer is proposed to serve as a microphone in the fully implantable cochlear prosthetic system which can be surgically implanted at the middle ear bone structure. Therefore, the design parameters such as size, weight and resonant frequency require deliberation. The paper presents a novel analytical model considering the impact of the mechanical amplification along with the width of the microlever and the capacitive fringe effects on the performance of the sensor. The design is simulated and verified using COMSOL MULTIPHYSICS 4.2. The accelerometer is designed within a sensing area of 1 mm2 and accomplishes a nominal capacitance of 4.85 pF and an excellent sensitivity of 5.91 fF/g.
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Research funding: Authors state no funding involved from any public, commercial or not-for-profit agencies.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Conflict of interest: Authors state no conflict of interest.
References
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© 2020 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
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- EEG Source Imaging (ESI) utility in clinical practice
- Research articles
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- Scalp electroencephalography (sEEG) based advanced prediction of epileptic seizure time and identification of epileptogenic region
- A simple model to detect atrial fibrillation via visual imaging
- Insertion torque/time integral as a measure of primary implant stability
- A microelectromechanical system (MEMS) capacitive accelerometer-based microphone with enhanced sensitivity for fully implantable hearing aid: a novel analytical approach
- Integrating artificial neural network and scoring systems to increase the prediction accuracy of patient mortality and organ dysfunction
- Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia
Articles in the same Issue
- Frontmatter
- Reviews
- A review of foot pose and trajectory estimation methods using inertial and auxiliary sensors for kinematic gait analysis
- EEG Source Imaging (ESI) utility in clinical practice
- Research articles
- A multi-source co-frequency stimulus method for electroencephalogram (EEG) enhancement
- Analysis of statistical coefficients and autoregressive parameters over intrinsic mode functions (IMFs) for epileptic seizure detection
- Scalp electroencephalography (sEEG) based advanced prediction of epileptic seizure time and identification of epileptogenic region
- A simple model to detect atrial fibrillation via visual imaging
- Insertion torque/time integral as a measure of primary implant stability
- A microelectromechanical system (MEMS) capacitive accelerometer-based microphone with enhanced sensitivity for fully implantable hearing aid: a novel analytical approach
- Integrating artificial neural network and scoring systems to increase the prediction accuracy of patient mortality and organ dysfunction
- Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia
