PhD Candidate
University of California, Berkeley
Areas of Interest
- Physical Electronics
Poster
Materials, Device, and Array Design for Next-Generation Ferroelectric Memory Technologies
Abstract
In this work, I will present the ground-up development of ferroelectric CMOS-compatible oxides (doped hafnium oxide, or HfO2), device design based on these ferroelectric oxides (from ferroelectric capacitors to FeFETs, or ferroelectric transistors), to an experimental demonstration of a content addressable memory (CAM) cell based on FeFETs. I will discuss the various engineering challenges associated with developing a CMOS-compatible ferroelectric oxide for memory applications, device-specific performance challenges (endurance, reliability, etc.), and steps taken to mitigate some of these bottlenecks. The end goal is to develop a nonvolatile memory element that can be used for embedded memory applications or for in-memory computing. The operational properties of doped HfO2-based FeFETs in terms of fast write/read speeds, low voltage requirements, and retention robustness make them well-suited to accommodate demanding modern computational needs by sealing the gaps between conventional memory, logic, and continued device scaling.
Bio
Ava J. Tan is a Ph.D. Candidate in the Electrical Engineering & Computer Sciences department at the University of California, Berkeley, advised by Professor Sayeef Salahuddin. Previously, she received her B.S. in Electrical & Computer Engineering from Cornell University in 2016. Her current research focuses on the realization of ferroelectric hafnium oxide-based nonvolatile memories for novel computing schemes, which encompasses materials development/optimization, device processing, and electrical characterization.
Ava is a recipient of the National Defense Science & Engineering Graduate Fellowship and the Lam Research Graduate Fellowship. She received the Best in Session Award at TECHCON 2017, the Best Poster Award at the 2017 Lam Research Technical Symposium, the Best Student Paper Award at the 2019 VLSI-TSA Symposium, and the Best Student Paper Award at the 2020 Device Research Conference. Her work on ferroelectric memories has been spotlighted in IEEE Spectrum Magazine.
