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Micromachines
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Advancing MEMS/MOEMS Sensors: AI-Driven Optimization and Emerging Applications
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Advancing MEMS/MOEMS Sensors: AI-Driven Optimization and Emerging Applications

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A：Physics".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 10057

Special Issue Editor

Dr. Qianbo Lu

E-Mail Website
Guest Editor
Institute of Flexible Electronics (IFE), Northwestern Polytechnical University, Xi'an 710072, China
Interests: sensors; optics; MEMS; ‪MOEMS‬; ‪flexible electronics‬‬‬

Special Issue Information

Dear Colleagues,

MEMS/MOEMS (Micro-Electro-Mechanical Systems/Micro-Opto-Electro-Mechanical Systems) inertial sensors—encompassing accelerometers, gyroscopes, and inertial measurement units (IMUs)—have transformed navigation, motion sensing, and stabilization technologies across diverse sectors, ranging from consumer electronics and automotive systems to aerospace and biomedical applications. The escalating demand for enhanced precision, miniaturization, energy efficiency, and environmental resilience has exposed the limitations of conventional design paradigms in reconciling the intricate trade-offs among performance, reliability, and manufacturability. Emerging intelligent design methodologies, empowered by machine learning, topology optimization, multi-physics co-design, and bio-inspired strategies, hold transformative potential to surmount these challenges and usher in next-generation sensor capabilities.

Recent breakthroughs underscore the adoption of AI-driven optimization for dynamic performance enhancement, novel materials (e.g., metamaterials, 2D materials) for superior sensitivity, and hybrid MEMS/MOEMS architectures that synergize optical and mechanical sensing modalities. Nevertheless, persistent challenges include (1) the complexity of multiphysics coupling (mechanical, optical, thermal, electrical), which compromises modeling fidelity; (2) fabrication-induced variability that adversely affects production yield; and (3) environmental perturbations (e.g., temperature fluctuations, mechanical vibrations) that impair long-term stability. Additionally, transitioning intelligent design frameworks from simulation to real-world deployment necessitates resolving critical gaps in computational efficiency, experimental validation, and compatibility with industry standards.

This Special Issue seeks cutting-edge contributions on intelligent design innovations for MEMS/MOEMS inertial sensors. Topics of interest may include, but are not limited to, the following:

  • AI/ML-aided design optimization and autonomous tuning;
  • Multiphysics modeling, digital twins, and uncertainty quantification;
  • Advanced materials (e.g., heterostructures, piezoelectrics) and their sensor integration;
  • Noise suppression and drift compensation techniques;
  • Self-calibrating and fault-tolerant architectures;
  • System-level co-design for emerging applications (e.g., IoT, robotics, wearables).

Submissions emphasizing scalability, reliability validation, and experimental benchmarking of theoretical models are particularly encouraged.

Dr. Qianbo Lu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent design of MEMS/MOEMS: AI/ML-driven optimization, topology optimization, bio-inspired design
  • multi-physics modeling: coupled mechanical-optical-thermal-electrical analysis, digital twins
  • sensor architectures: accelerometers, gyroscopes, IMUs, hybrid MEMS/MOEMS
  • advanced materials: metamaterials, 2D materials, piezoelectrics, nanocomposites
  • performance enhancement: noise reduction, self-calibration, sensitivity optimization

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Published Papers (8 papers)

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Research

Jump to: Review

12 pages, 2836 KB  
Article
A Wafer-Level Stacking Scheme Based on Hybrid Etching and Low-Temperature Bonding for High-Performance MEMS Devices
by Pengfei Li, Xin Yan, Yunjie Yang, Leilei Meng, Xiwen Zhang, Haiyan Wang and Qianbo Lu
Micromachines 2026, 17(6), 651; https://doi.org/10.3390/mi17060651 - 25 May 2026
Abstract
Silicon micromachining serves as the foundational enabling technology for high-precision MEMS inertial sensors. However, the relentless pursuit of enhanced sensitivity and multi-functionality in emerging applications encounters a fundamental bottleneck when confined to two-dimensional scaling. The evolution toward complex three-dimensional (3D) stacking architectures is [...] Read more.
Silicon micromachining serves as the foundational enabling technology for high-precision MEMS inertial sensors. However, the relentless pursuit of enhanced sensitivity and multi-functionality in emerging applications encounters a fundamental bottleneck when confined to two-dimensional scaling. The evolution toward complex three-dimensional (3D) stacking architectures is an inevitable trajectory for devices including MEMS inertial sensors, yet performance is constrained by the limitations of conventional processes in fabricating and integrating intricate 3D hollow structures. Specifically, uniformity in large-area deep silicon etching, structural integrity of convex corners in wet etching, and residual stress induced by multi-layer wafer bonding have emerged as critical, shared challenges. To address these issues, this paper proposes a triple-layer wafer-level stacking scheme that synergistically combines wet/dry hybrid etching with low-temperature adhesive bonding. This stacking scheme incorporates an innovative linear compensation model for wet-etched convex corners, enabling high-precision fabrication of complex corner structures under deep etching conditions. Furthermore, a collaborative strategy involving temporary bonding and plasma flow-field optimization improves the uniformity and integrity of dry etching for large perforated structures. A low-temperature triple-layer wafer-level stacking process is developed, encompassing precise adhesive dispensing, optical alignment, and a stepped low-temperature curing profile, thereby achieving highly symmetric 3D integration with controlled adhesive distribution. The efficacy of this stacking scheme is validated through the fabrication of a symmetrically stacked triple-layer MOEMS accelerometer sensing element. Test results demonstrate a noise floor as low as 0.40 µg/√Hz and a bias instability of 1.81 µg over 10 min. Compared with a double-layer counterpart, improved performance is obtained. The wafer-level stacking scheme established in this work not only provides a viable pathway for pushing the manufacturing limits of high-precision inertial devices but also offers a generic methodology for tackling complex hollow structure formation and low-temperature integration, holding referential value for broader applications in high-precision 3D microsystems. Full article
(This article belongs to the Special Issue Advancing MEMS/MOEMS Sensors: AI-Driven Optimization and Emerging Applications)
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17 pages, 4195 KB  
Article
Design and Implementation of a Low-Noise Analog Front-End Circuit for MEMS Capacitive Accelerometers
by Keru Gong, Jiacheng Li, Xiaoyi Wang, Huiliang Cao and Huikai Xie
Micromachines 2026, 17(3), 378; https://doi.org/10.3390/mi17030378 - 20 Mar 2026
Viewed by 613
Abstract
This paper presents a low-noise analog front-end (AFE) integrated circuit (IC) circuit for capacitive micro-electromechanical system (MEMS) accelerometers that can be used for optical image stabilization (OIS) in various optical imaging systems. The AFE circuit design features a fully differential chopper stabilization technique [...] Read more.
This paper presents a low-noise analog front-end (AFE) integrated circuit (IC) circuit for capacitive micro-electromechanical system (MEMS) accelerometers that can be used for optical image stabilization (OIS) in various optical imaging systems. The AFE circuit design features a fully differential chopper stabilization technique that efficiently minimizes low-frequency 1/f noise and parasitic coupling. The AFE circuit chip is fabricated in a 0.18 μm complementary metal-oxide-semiconductor (CMOS) technology and co-packaged with an x-axis capacitive MEMS accelerometer based on a silicon-on-glass (SOG) process. The SOG accelerometer has a footprint of 1000 μm × 950 μm. The packaged system demonstrates a sensitivity of 342 mV/g and a nonlinearity of 1.1% between −1 g and +1 g, a dynamic range of 88 dB, and an equivalent noise floor of 14 μg/Hz. Full article
(This article belongs to the Special Issue Advancing MEMS/MOEMS Sensors: AI-Driven Optimization and Emerging Applications)
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15 pages, 10540 KB  
Article
Piezoelectric Thin-Film Actuator for Dynamic Tuning of Micro-Optical Cavities
by Dehua Tan, Pengfei Li, Xuyang Zhou, Qingxiong Xiao, Chaohui Wu, Qixuan Zhu, Miao Lei, Ting Li and Qianbo Lu
Micromachines 2026, 17(3), 345; https://doi.org/10.3390/mi17030345 - 12 Mar 2026
Viewed by 616
Abstract
In micro-opto-electro-mechanical systems (MOEMS), the micro-optical cavity plays a pivotal role. As performance requirements for MOEMS devices continue to rise, these cavities must achieve higher performance levels while simultaneously reducing their physical footprint. However, existing high-precision micro-optical cavities face challenges such as high [...] Read more.
In micro-opto-electro-mechanical systems (MOEMS), the micro-optical cavity plays a pivotal role. As performance requirements for MOEMS devices continue to rise, these cavities must achieve higher performance levels while simultaneously reducing their physical footprint. However, existing high-precision micro-optical cavities face challenges such as high process sensitivity and conflicting trade-offs between dynamic range and precision. To address these issues, piezoelectric thin-film actuators present a viable solution due to their high precision, stroke flexibility, electromagnetic interference resistance, and structural scalability. This study proposes a piezoelectric thin-film actuator based on the d33 mode. The device adopts an island-circular structure that integrates a lead zirconate titanate (PZT) piezoelectric film with metal electrodes. By employing particle swarm optimization (PSO) to enhance displacement output and anti-gravity capabilities, the actuator achieves displacement outputs below 100 nm within a compact form factor while maintaining nanometer-level resolution. Simulation and experimental results confirm a first-order natural frequency of approximately 5.8 kHz, along with a reasonable linear displacement response across a 4–6 V drive voltage range. Furthermore, the device demonstrates functionality within a Fabry–Pérot (F-P) microcavity system, enabling active optical path length modulation through precise cavity tuning. This research provides an effective approach to enhancing the dynamic performance and process compatibility of micro-optical cavity devices, advancing the development of next-generation MOEMS systems. Full article
(This article belongs to the Special Issue Advancing MEMS/MOEMS Sensors: AI-Driven Optimization and Emerging Applications)
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13 pages, 2213 KB  
Article
Experimental Characterization and Calibration of a MEMS Electric Field Sensor Under DC Ionized Field Conditions
by Ren Ren, Bing Li and Chunrong Peng
Micromachines 2026, 17(3), 317; https://doi.org/10.3390/mi17030317 - 3 Mar 2026
Viewed by 465
Abstract
Accurate electric field measurement in high-voltage direct current (HVDC) environments is essential for power system monitoring. This study systematically investigates the output characteristics of a micro-electro-mechanical system (MEMS) electric field sensor under DC ionized field conditions. Using a controlled experimental platform capable of [...] Read more.
Accurate electric field measurement in high-voltage direct current (HVDC) environments is essential for power system monitoring. This study systematically investigates the output characteristics of a micro-electro-mechanical system (MEMS) electric field sensor under DC ionized field conditions. Using a controlled experimental platform capable of generating independent nominal electric fields and ion flows, the influence of ion current density on sensor sensitivity and offset was quantitatively analyzed. Experimental results reveal that ion flow leads to a progressive output drift and significant measurement deviations when using conventional electrostatic calibration. To address this issue, a joint calibration method incorporating ion current density is proposed. Validation experiments demonstrate that the proposed method significantly improves measurement accuracy, reducing the maximum relative error from 29.28% to approximately 5.07%. This work provides a reliable experimental basis and calibration methodology for utilizing MEMS electric field sensors in complex ionized DC environments. Full article
(This article belongs to the Special Issue Advancing MEMS/MOEMS Sensors: AI-Driven Optimization and Emerging Applications)
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20 pages, 3062 KB  
Article
Investigation of III-Nitride MEMS Pressure Sensor for High-Temperature Applications
by Makhluk Hossain Prio, Maruf Morshed, Lavanya Muthusamy, Md Sohanur E. Hijrat Raju, Itmenon Towfeeq, Durga Gajula and Goutam Koley
Micromachines 2026, 17(2), 177; https://doi.org/10.3390/mi17020177 - 28 Jan 2026
Viewed by 1177
Abstract
High-temperature operation of AlGaN/GaN Heterojunction Field Effect Transistor embedded diaphragm-based MEMS pressure sensors have been investigated, which utilized their wide bandgap and piezo resistivity to perform stably at elevated temperatures. The performance of the pressure sensor was observed over a change in applied [...] Read more.
High-temperature operation of AlGaN/GaN Heterojunction Field Effect Transistor embedded diaphragm-based MEMS pressure sensors have been investigated, which utilized their wide bandgap and piezo resistivity to perform stably at elevated temperatures. The performance of the pressure sensor was observed over a change in applied pressure of 35 kPa, which resulted in an experimentally measured change in drain–source resistance (ΔRDS/RDS(0)) of 0.32% at room temperature and 0.65% at 250 °C, respectively. Additionally, the COMSOL-based Finite Element (FE) Simulations, in conjunction with our developed theoretical model, was utilized to theoretically determine the change in drain–source resistance. This theoretically calculated ΔRDS/RDS(0) of 0.45% at room temperature closely aligns with the experimental observations. Moreover, the sensor exhibited a gate-bias-dependent tunability, with the enhancement of sensitivity under increasingly negative gate voltages. Furthermore, the sensors demonstrated a stable and repeatable sensing operation over multiple pressure cycles up to 300 °C, with a rapid response time of <10 ms, suggesting excellent potential for reliable, high-performance pressure sensing in harsh, high-temperature environments. Full article
(This article belongs to the Special Issue Advancing MEMS/MOEMS Sensors: AI-Driven Optimization and Emerging Applications)
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16 pages, 4549 KB  
Article
Miniature Electromagnetic and Mechanical Resonators for Measurements of Acceleration with the Help of Nitrogen-Vacancy Color Centers
by Marina Rezinkina, Oleg Rezinkin, Fedor Jelezko and Claus Braxmaier
Micromachines 2025, 16(12), 1311; https://doi.org/10.3390/mi16121311 - 23 Nov 2025
Viewed by 2776
Abstract
Using mathematical and physical modeling, we investigate the influence of the configuration and parameters of miniature electromagnetic and mechanical resonators on their output characteristics. Such electromagnetic resonators are required for the microwave excitation of nitrogen-vacancy color centers, which are used as sensors for [...] Read more.
Using mathematical and physical modeling, we investigate the influence of the configuration and parameters of miniature electromagnetic and mechanical resonators on their output characteristics. Such electromagnetic resonators are required for the microwave excitation of nitrogen-vacancy color centers, which are used as sensors for various physical quantities, including acceleration, force, and magnetic field induction. The mechanical resonators under consideration are designed for measuring acceleration using nitrogen–vacancy color centers. As a result of these studies, we selected the types of miniature electromagnetic and mechanical resonators that ensure the efficient operation of nitrogen–vacancy color center sensors. Full article
(This article belongs to the Special Issue Advancing MEMS/MOEMS Sensors: AI-Driven Optimization and Emerging Applications)
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Review

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21 pages, 6727 KB  
Review
Overview in Machine-Learning-Assisted Sensing Techniques for Monitoring COVID-19
by Yan Feng and Ming La
Micromachines 2026, 17(3), 283; https://doi.org/10.3390/mi17030283 - 25 Feb 2026
Cited by 1 | Viewed by 629
Abstract
Viruses suddenly emerging from obscurity or anonymity affect our quality of life and increase incidence rate and mortality. A typical example is the global coronavirus disease 2019 (COVID-19) pandemic. Although severe acute respiratory syndrome coronavirus 2, known as the pathogen of COVID-19 has [...] Read more.
Viruses suddenly emerging from obscurity or anonymity affect our quality of life and increase incidence rate and mortality. A typical example is the global coronavirus disease 2019 (COVID-19) pandemic. Although severe acute respiratory syndrome coronavirus 2, known as the pathogen of COVID-19 has been significantly eliminated, its monitoring is still crucial, as the infectious disease may break out again. Therefore, it is necessary to develop simple and effective tools for monitoring COVID-19 and other diseases. Here, we summarize the progress of machine-learning-based biosensors in the monitoring and management of COVID-19. This article mainly includes three sections: machine learning algorithms, machine-learning-assisted biosensors, and challenges and future perspectives. We believe that this work is valuable for developing artificial-intelligence-based innovative analytical devices for healthcare monitoring and management of COVID-19 and other infectious diseases. Full article
(This article belongs to the Special Issue Advancing MEMS/MOEMS Sensors: AI-Driven Optimization and Emerging Applications)
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42 pages, 4490 KB  
Review
Continuous Monitoring with AI-Enhanced BioMEMS Sensors: A Focus on Sustainable Energy Harvesting and Predictive Analytics
by Mingchen Cai, Hao Sun, Tianyue Yang, Hongxin Hu, Xubing Li and Yuan Jia
Micromachines 2025, 16(8), 902; https://doi.org/10.3390/mi16080902 - 31 Jul 2025
Cited by 9 | Viewed by 2957
Abstract
Continuous monitoring of environmental and physiological parameters is essential for early diagnostics, real-time decision making, and intelligent system adaptation. Recent advancements in bio-microelectromechanical systems (BioMEMS) sensors have significantly enhanced our ability to track key metrics in real time. However, continuous monitoring demands sustainable [...] Read more.
Continuous monitoring of environmental and physiological parameters is essential for early diagnostics, real-time decision making, and intelligent system adaptation. Recent advancements in bio-microelectromechanical systems (BioMEMS) sensors have significantly enhanced our ability to track key metrics in real time. However, continuous monitoring demands sustainable energy supply solutions, especially for on-site energy replenishment in areas with limited resources. Artificial intelligence (AI), particularly large language models, offers new avenues for interpreting the vast amounts of data generated by these sensors. Despite this potential, fully integrated systems that combine self-powered BioMEMS sensing with AI-based analytics remain in the early stages of development. This review first examines the evolution of BioMEMS sensors, focusing on advances in sensing materials, micro/nano-scale architectures, and fabrication techniques that enable high sensitivity, flexibility, and biocompatibility for continuous monitoring applications. We then examine recent advances in energy harvesting technologies, such as piezoelectric nanogenerators, triboelectric nanogenerators and moisture electricity generators, which enable self-powered BioMEMS sensors to operate continuously and reducereliance on traditional batteries. Finally, we discuss the role of AI in BioMEMS sensing, particularly in predictive analytics, to analyze continuous monitoring data, identify patterns, trends, and anomalies, and transform this data into actionable insights. This comprehensive analysis aims to provide a roadmap for future continuous BioMEMS sensing, revealing the potential unlocked by combining materials science, energy harvesting, and artificial intelligence. Full article
(This article belongs to the Special Issue Advancing MEMS/MOEMS Sensors: AI-Driven Optimization and Emerging Applications)
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