670889 event 1699048275 1699048304 <![CDATA[Ph.D. Proposal Oral Exam - Yaoyao (Emma) Long]]> Title:  High-Performance 4H-SiC MEMS with Machine Learning-aided Temperature Compensation and Calibration

Committee: 

Dr. Ayazi, Advisor

Dr. Ghalichechian, Chair

Dr. Hao

]]> The objective of the proposed research is to revolutionize the field of Micro-Electro-Mechanical Systems (MEMS) by developing high-performance 4H-Silicon Carbide (4H-SiC) MEMS resonators and gyroscopes with machine learning-aided temperature compensation and calibration. The research specifically targets the longstanding challenges of temperature-induced frequency drift and material softening in 4H-SiC MEMS devices. By employing machine learning algorithms, the project seeks to dynamically model and correct temperature-related variations in real-time, thereby enhancing device stability and performance. The methodology involves a multi-phase approach that includes material characterization, device fabrication, and machine learning model development. Anticipated outcomes include a 4H-SiC MEMS resonator and gyroscope with significantly reduced temperature-induced frequency drift and a machine-learning model for real-time temperature compensation. This research has the potential to set new benchmarks in MEMS-based timekeeping and inertial sensing technologies, impacting various sectors such as telecommunications, aerospace, and healthcare.

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