non-volatile memory

Women-in-Engineering Seminar Series – Seminar 2

Women-in-Engineering Seminar Series – Seminar 2 150 150 ieeeeduweek

Title: Spins for a New Computing Era

Abstract: Cognitive computing will redefine everyday life, changing how individuals perform their jobs, interact with others, and make decisions. Nonvolatile memories hold the key to solve the overwhelming energy demand for such computing and ensure intelligent systems for sustainable future. Magnetic memory, with electron spin as the information token, is one of the most promising nonvolatile technologies for next generation computers.  

I will present a complete path – from spintronic materials to device, for future computing era. The experimental demonstration of spin orbit torque induced magnetic devices will be shown as the building blocks (i.e., synapses and neurons) for in-memory computing. The synaptic device shows the most important functionality – linear output resistance, and the neurons provide programmable nonlinearity, unlike any other non-volatile memories. The scaled devices can potentially achieve energy consumption comparable to biological synapses, which is 1000× lower than any other technologies. Later, the study of alternate magnetic materials will be shown to identify more energy-efficient and dynamically robust superior materials for sub-nanosecond devices for non-von Neumann computation. The devices and materials developed in this work extend in applications beyond the examples provided here, introducing versatile platforms for using electron spin in other microelectronic applications like communication and quantum computers. 

Speaker bio: Saima Siddiqui is a DRIVE postdoctoral fellow in the Department of Materials Science and Engineering at University of Illinois at Urbana-Champaign. Prior to that, she was a postdoctoral researcher in Materials Science Division at Argonne National Laboratory. Her research interests lie in quantum materials and nanoscale devices with unique functionalities by combining high quality materials growth, innovative fabrication, integration, and characterization techniques. 

Saima completed her Ph.D. in Electrical Engineering and Computer Science at Massachusetts Institute of Technology in 2018 working on spintronic devices for emerging frontiers in computing. She received her Bachelor of Science in Electrical and Electronic Engineering at Bangladesh University of Engineering and Technology. Saima is a recipient of the Illinois Distinguished Postdoctoral Fellowship and 2021 IEEE Chicago Early Career Award in Magnetics and has been selected as an EECS Rising Star in 2019.

Women-in-Engineering Seminar Series – Seminar 1

Women-in-Engineering Seminar Series – Seminar 1 150 150 ieeeeduweek

Abstract: Data stored in the cloud or on mobile devices reside in physical memory systems with finite sizes. Today, huge amounts of analog data, e.g., images, sounds and videos, are first converted into binary digital form and then information compression algorithms (source coding, e.g. the JPEG standard) and error-correcting codes (channel coding) are separately employed to minimize the amount of physical storage required.

In this talk, I will present a new concept for directly storing analog data in a compressed format into analog-valued memory. This new concept provides more efficient storage of analog data by combining the use of joint source channel coding (JSCC) from information theory with the use of emerging non-volatile memories (e.g., Phase-change Memory (PCM) and Resistive RAM (RRAM)) that can directly store analog values as continuously tuned physics properties (e.g., resistances). Specifically, I will describe how we develop an adaptive JSCC scheme with neural network for lossy image compression and storage in analog-valued non-volatile memory arrays. Our experiments using PCM and RRAM array chips show competitive performance for storing analog data using this concept. I will also show JSCC provides resilience to the PCM and RRAM device technology non-idealities, including defective cells, device variability, resistance drift, and relaxation. We ho`pe this work can open up new opportunities for addressing pressing demands for the storage of analog data.

Speaker bio: Xin Zheng is a final-year PhD student in Electrical Engineering at Stanford University, advised by Prof. H. -S. Philip Wong. She received her B.S. degree in Physics from Nanjing University, and her M.S. degree in Electrical Engineering from Stanford University. Her PhD research focuses on analog storage system enabled by emerging memory technologies (e.g., RRAM and PCM), with technical efforts spanning from memory device characterization and modeling, memory array chip design and tape-out, to analog signal coding scheme development.