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Abstract: Advances in artificial intelligence have led to increasing demand for energy. A potential solution for the energy requirement has emerged in the form of brain-inspired analog and in-memory computing. However, the non-ideal behavior of this technology has hindered its widespread adoption, necessitating the optimization of materials and devices. This talk will explore the potential of adaptive oxide brain-inspired devices by delving into their mechanism and analyzing their behavior. Specifically, it will examine CMOS-compatible HfOx-based brain-inspired devices using a compact electron transport model to understand the physics. Furthermore, the talk will focus on optimizing these devices by utilizing an emerging ultra-thin layered material with unique properties as the electrode and capping layer to improve device performance. Finally, the talk will conclude by demonstrating transfer learning on an analog brain-inspired chip.

Bio: Fabia Farlin Athena is an Electrical and Computer Engineering (ECE) Ph.D. candidate at Georgia Institute of Technology, advised by Professor Eric M. Vogel. She also received her MS in ECE from Georgia Tech. Fabia's Ph.D. research focuses on studying emerging materials and devices that promote energy-efficient computing. During her Ph.D., she has also interned twice at IBM T.J. Watson Research Center. Before starting at Georgia Tech, Fabia worked at Purdue University for two semesters as a graduate researcher and collaborated with Idaho National Lab on nuclear materials for next-generation energy. Fabia completed her BS in Materials Science and Engineering at the Bangladesh University of Engineering and Technology, graduating second in her class. Her research has been recognized with the Georgia Tech ECE Ph.D. Fellowship, Cadence Diversity in Technology Scholarship, EECS Rising Stars 2023, Colonel Oscar P. Cleaver Award for the most outstanding Ph.D. dissertation proposal in Georgia Tech ECE, MRS Graduate Student Award Finalist for Fall 2023, and IBM Ph.D. Fellowship 2022-24.