Chua Memristor CenterDr. R. Stanley Williams
Department of Electrical and Computer Engineering
Texas A&M University
301 Wisenbaker Engineering Building
College Station, TX 77843-3128
Email: rstanleywilliams@tamu.edu
Dr. R. Stanley Williams is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he holds the Hewlett Packard Enterprise Company Chair. He earned his B.A. in Chemical Physics from Rice University and his Ph.D. in Physical Chemistry from the University of California, Berkeley.
A world-renowned nanotechnology and computing researcher, Dr. Williams is best known for his pioneering work on memristors and neuromorphic computing. His research spans nanoelectronics, nonlinear dynamics, analog computation, and brain-inspired architectures. He currently leads the Energy Frontier Research Center project “REMIND,” focused on reconfigurable electronic materials mimicking neural dynamics.
Dr. Williams has authored hundreds of high-impact publications and holds numerous patents. His contributions have been recognized with prestigious awards, including the Feynman Prize in Nanotechnology and listings among top electronics visionaries by EE Times.
“Chaotic dynamics in nanoscale NbO₂ Mott memristors for analogue computing”
Authors: S. Kumar, J. P. Strachan, R. S. Williams
Journal: Nature, 2017
Summary: This paper explores the chaotic behavior in nanoscale NbO₂ Mott memristors and their potential applications in analog computing.
Link: https://doi.org/10.1038/nature23307
“A Family of Stateful Memristor Gates for Complete Cascading Logic”
Authors: K. M. Kim, R. S. Williams
Journal: IEEE Transactions on Circuits and Systems I, 2019
Summary: The authors present a family of stateful memristor gates capable of complete cascading logic, advancing the design of memristor-based circuits.
Link: https://doi.org/10.1109/TCSI.2019.2926811
“Analog signal and image processing with large memristor crossbars”
Authors: C. Li, M. Hu, Y. Li, H. Jiang, N. Ge, E. Montgomery, N. Dávila, Z. Li, J. P. Strachan, P. Lin, W. Song, Z. Wang, M. Barnell, Q. Wu, R. S. Williams, J. J. Yang, Q. Xia
Journal: Nature Electronics, 2018
Summary: This publication demonstrates the use of large memristor crossbars for analog signal and image processing, showcasing their potential in neuromorphic computing.
Link: https://doi.org/10.1038/s41928-017-0002-z
“Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing”
Authors: Z. Wang, S. Joshi, S. E. Savel’ev, H. Jiang, R. R. Rizwan, P. Lin, H. L. Xin, Q. Wu, M. Barnell, M. Hu, N. Ge, J. P. Strachan, Z. Li, R. S. Williams, Q. Xia, J. J. Yang
Journal: Nature Materials, 2017
Summary: The paper discusses memristors exhibiting diffusive dynamics, serving as synaptic emulators in neuromorphic computing systems.
Link: https://doi.org/10.1038/nmat4756
“A scalable neuristor built with Mott memristors”
Authors: M. D. Pickett, G. Medeiros-Ribeiro, R. S. Williams
Journal: Nature Materials, 2013
Summary: This work introduces a scalable neuristor using Mott memristors, contributing to the development of neuromorphic computing architectures.
Link: https://doi.org/10.1038/nmat3510
“Memristive switching mechanism for metal/oxide/metal nanodevices”
Authors: J. J. Yang, M. D. Pickett, X. Li, D. A. A. Ohlberg, D. R. Stewart, R. S. Williams
Journal: Nature Nanotechnology, 2008
Summary: The authors investigate the switching mechanism in metal/oxide/metal nanodevices, providing insights into memristive behavior.
Link: https://doi.org/10.1038/nnano.2008.160
“The missing memristor found”
Authors: D. B. Strukov, G. S. Snider, D. R. Stewart, R. S. Williams
Journal: Nature, 2008
Summary: This landmark paper reports the discovery of the memristor, a fundamental passive circuit element, bridging the gap between theory and practical realization.
Link: https://doi.org/10.1038/nature06932
“A Defect-Tolerant Computer Architecture: Opportunities for Nanotechnology”
Authors: J. R. Heath, P. J. Kuekes, G. S. Snider, R. S. Williams
Journal: Science, 1998
Summary: The paper presents a defect-tolerant computer architecture, highlighting opportunities for nanotechnology in computing systems.
Link: https://doi.org/10.1126/science.280.5370.1716
“Memristive switches enable stateful logic operations via material implication”
Authors: J. L. Borghetti, G. S. Snider, P. J. Kuekes, J. J. Yang, D. R. Stewart, R. S. Williams
Journal: Nature, 2010
Summary: This work demonstrates how memristive switches can perform stateful logic operations using material implication, paving the way for new computing paradigms.
Link: https://doi.org/10.1038/nature08940
