Magnetic and Spintronic Device for Computing and Memory

Presented online October 13, 2020, 12:00 p.m.-1:00 p.m. at Nano@Tech Virtual Fall 2020.Hosted by Professor Asif Khan, School of Electrical and Computer Engineering, Georgia Tech.Saima Siddiqui is a postdoctoral associate at University of Illinois at Urbana Champaign in the Department of Materials Science and Engineering. After completing her PhD in Electrical Engineering from MIT in 2018, she spent nine months at Argonne National Laboratory as a postdoctoral researcher in the Materials Science division. Her research focus is to explore novel physical phenomena of electron’s spin in quantum materials and implement them in building Boolean and non-Boolean devices for next generation energy-efficient computing. She has been selected as an EECS rising star in 2019.Runtime: 53:05 minutesMagnetic devices promise intriguing design paradigms where electron spin is used as the information token instead of its charge counterpart. While magnetic random-access memory (MRAM) is considered one of the most mature nonvolatile memory technologies for next generation computers, spin-based devices can be a game-changing option for beyond-CMOS and in-memory computing. In the future cognitive era, nonvolatile memories hold the key to overcome the bottleneck in the computational performance due to data shuttling between the processing and the memory units. The application of spintronic devices for cognitive applications requires versatile, scalable device design that is adaptable to emerging material physics. Spin-orbit torque driven magnetic tunnel junction has emerged as one of the most promising candidates for energy-efficient nonvolatile logic and memory devices. In this talk, I will discuss the design-space of spintronics devices as the key building blocks for in-memory computing and benchmark the performance metrics with other state-of-the-art non-volatile memories. I will show the first experimental demonstrations of linear synaptic weight generator and the nonlinear activation function generator integrated in a single device and operating with sub-10 ns pulses. The introduction of antiferromagnetic materials in these devices promises to enable even picosecond operations. A complete neuromorphic hardware accelerator using nonvolatile magnetic devices can revolutionize computer architectures by embedding memory into logic circuits in a fine-grained fashion