Key research themes
1. How can noise characteristics be utilized to assess the quality and reliability of MEMS devices?
This research area focuses on investigating noise analysis as a diagnostic tool to monitor the quality and reliability degradation in MEMS devices. Given MEMS microstructures’ diversity in materials, mechanisms, and transduction modes, noise signals encompass multiple complex sources including electrical and mechanical components. Understanding and decoupling these noise contributions can offer valuable predictive insights into device health, enabling early fault detection and improved lifecycle management critical for safety-critical applications.
2. What are the recent developments and challenges in modeling and compensating MEMS sensor errors to enhance performance in inertial navigation?
This line of research centers on precise error modeling of MEMS inertial sensors including accelerometers and gyroscopes, which suffer from significant biases, drifts, and stochastic noise limiting their utility in navigation systems. Advanced modeling approaches—including statistical, stochastic, and machine learning methods—seek to characterize deterministic and random error components accurately, enabling real-time compensation that can bring low-cost MEMS sensors closer to high-grade sensor performance.
3. How is MEMS technology advancing applications in medical devices and point-of-care diagnostic systems?
This research theme investigates how MEMS miniaturization and integration afford significant breakthroughs in medical tools—enabling minimally invasive procedures, in situ physiological monitoring, and portable diagnostic devices. Research spans microneedle fabrication for drug delivery and neural interface, miniaturized sensors for internal body monitoring, and MEMS-enabled point-of-care testing platforms harnessing microfluidics and bioMEMS for rapid, accurate, and decentralized medical diagnostics.