ArticleMicrosystems & nanoengineering2025
Electrostatic anti-spring-enhanced MEMS accelerometer with auto-tuning capability.
Article in Microsystems & nanoengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Nanosecond, Nanometer Movies of MEMS/NEMS Dynamics in a Standard SEM Using Time-Tagged Secondary Electrons.Small methods · 2026Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
This study introduces a MEMS accelerometer equipped with an adaptive tuning system for an electrostatic anti-spring. As the input acceleration increases, the sensitivity of the adaptive MEMS accelerometer decreases to compensate for the measurement range. It leverages the benefits of both conventional open- and closed-loop accelerometer designs. Comprehensive theoretical analyses and experimental tests are conducted, showing consistency between theory and experimental results. In comparison to conventional MEMS accelerometer designs, this novel MEMS accelerometer demonstrates enhanced performance. With an actuation voltage of 15.4 V and under 0 g acceleration input, the sensitivity of the accelerometer improves from 1.28 V/g to 39.43 V/g, and the spring constant is reduced from 41.0 N/m to 1.38 N/m. The noise floor also decreases from 8628 ng/√Hz (at 100 Hz) to 279 ng/√Hz (at 100 Hz). The dynamic range enhances from 127 dB to 157 dB. Besides, a hybrid continuous-time interface is utilized to apply the actuation force on the sensing comb fingers. This approach not only simplifies the circuit design but also minimizes the required die area, power consumption. The combination of these features makes the novel MEMS accelerometer both highly sensitive and large measurement range, as a promising solution for various applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.