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Shan X. Wang

Emerging Memory Fundamentals

Shan X. Wang, Leland T. Edwards Professor in the School of Engineering, Stanford University

Smaller lithographic features in semiconductor devices has improved the speed of computing, but it has increased power consumption. A possible solution to limit power consumption is normally-off computing in which on-chip volatile memories are replaced with non-volatile memories. In-memory computing based on emerging Phase Change Memory (PCM), Resistive RAM (RRAM), Ferroelectric RAM (FRAM), and Magnetoresistive RAM (MRAM) can overcome the von Neumann bottleneck caused by the standard computer architecture in which the processor and memory are separate and data moves between the two. Therefore, we will discuss the principles and materials underlying these non-volatile memory technologies, providing working knowledge necessary to appreciate and create innovative future computing systems including Internet of Things (IOT), data center applications, and Edge AI. 

Compared to PCM, RRAM, and FRAM, Magnetoresistive RAM (MRAM) has arguably become a front runner in the emerging memory race. Since its conception in 1980s, MRAM has been a niche technology by and large until recently. Mass production of MRAM as a “mainstream” memory finally began in 2018-19 timeframe, after very intense R&D in the last two decades. In the 2nd half of the tutorial, I will give an overview of the major developments in MRAM technology over the past 30 years: The various MRAM technology generations, ranging from the original anisotropic magnetoresistive (AMR) RAM to present-day magnetic tunnel junction (MTJ) based MRAM have served useful functions as stand-alone devices. The emergence of spin-transfer torque MRAM (STT-MRAM), compatible with conventional CMOS processes, is underlying new embedded product developments.   The potential transition to spin-orbit torque (SOT) MRAM capable of operating in the sub-ns regime, could move MRAM technology even closer to DRAM and SRAM replacement, with sufficient manufacturing volume. Voltage Control Magnetic Anisotropy (VCMA) materials and novel topological materials with large spin-Hall angles are expected to further improve the density and speed of SOT-MRAM. A large array of such devices are very attractive for ultrafast MRAM and stochastic neural network applications

Dr. Wang is the Leland T. Edwards Professor in the School of Engineering, Stanford University. He is a Professor and Associate Chair of Materials Science & Engineering and jointly a Professor of Electrical Engineering, and by courtesy, a Professor of Radiology (Stanford School of Medicine). He directs the Center for Magnetic Nanotechnology and is a leading expert in biosensors, information storage and spintronics. His research and inventions span across a variety of areas including magnetic biochips, in vitro diagnostics, cancer biomarkers, magnetic nanoparticles, magnetic sensors, magnetoresistive random access memory, and magnetic integrated inductors.

He has over 290 publications, and holds 63 issued or pending patents in these and interdisciplinary areas. He was named an inaugural Fred Terman Fellow, and was elected a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of American Physical Society (APS) for his seminal contributions to magnetic materials and nanosensors. His team won the Grand Challenge Exploration Award from Gates Foundation (2010), the XCHALLENGE Distinguished Award (2014), and the Bold Epic Innovator Award from the XPRIZE Foundation (2017).

Dr. Wang cofounded three high-tech startups in Silicon Valley, including MagArray, Inc. and Flux Biosciences, Inc. In 2018 MagArray launched a first of its kind lung cancer early diagnostic assay based on LASSO logistic regression or support vector machine (SVM). Through his participation in the Center for Cancer Nanotechnology Excellence (as co-PI of the CCNE),  the Joint University Microelectronics Program (JUMP), and the Energy Efficient Electronic Science (E3S) Center, he is actively engaged in the application of artificial intelligence (AI) for biomedicine, and is developing emerging memories for energy efficient computing and AI hardware.

Dr. Wang obtained his PhD in Electrical and Computer Engineering from Carnegie Mellon University in 1993, MS in Physics from Iowa State University in 1988, and BS in Physics from the University of Science and Technology of China in 1986.