{"670889":{"#nid":"670889","#data":{"type":"event","title":"Ph.D. Proposal Oral Exam - Yaoyao (Emma) Long","body":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003ETitle:\u0026nbsp; \u003C\/span\u003E\u003C\/strong\u003E\u003Cem\u003E\u003Cspan\u003EHigh-Performance 4H-SiC MEMS with Machine Learning-aided Temperature Compensation and Calibration\u003C\/span\u003E\u003C\/em\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003ECommittee:\u0026nbsp; \u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. \u003C\/span\u003E\u003Cspan\u003EAyazi\u003C\/span\u003E\u003Cspan\u003E, Advisor\u003C\/span\u003E \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. \u003C\/span\u003E\u003Cspan\u003EGhalichechian\u003C\/span\u003E\u003Cspan\u003E, Chair\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. \u003C\/span\u003E\u003Cspan\u003EHao\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EThe 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.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"High-Performance 4H-SiC MEMS with Machine Learning-aided Temperature Compensation and Calibration"}],"uid":"28475","created_gmt":"2023-11-03 21:51:15","changed_gmt":"2023-11-03 21:51:44","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2023-11-09T09:30:00-05:00","event_time_end":"2023-11-09T11:30:00-05:00","event_time_end_last":"2023-11-09T11:30:00-05:00","gmt_time_start":"2023-11-09 14:30:00","gmt_time_end":"2023-11-09 16:30:00","gmt_time_end_last":"2023-11-09 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Room 523A, TSRB","extras":[],"related_links":[{"url":"https:\/\/gatech.zoom.us\/j\/99648293043?pwd=VTNTdU0yRU1tcS9ydStmL2dZSGRKQT09\u0026from=addon","title":"Zoom link"}],"groups":[{"id":"434371","name":"ECE Ph.D. Proposal Oral Exams"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"},{"id":"1808","name":"graduate students"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}