Technische Universität Dresden

R. Stanley Williams

R. Stanley Williams
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Prof. R. Stanley Williams

Department of Electrical and Computer Engineering
Texas A&M University
301 Wisenbaker Engineering Building
College Station, TX 77843-3128

Telephone: +1 979-845-7441

Email: rstanleywilliams@tamu.edu

Research Field and Activities

  • Memristors and Neuromorphic Computing
  • Ionic & Photonic Devices
  • Analog Computing
  • Cognitive Systems and Computation

Short Biography

Prof. 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.

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.

References / Publications

  • S. Kumar, J. P. Strachan, and R. S. Williams, “Chaotic dynamics in nanoscale NbO₂ Mott memristors for analogue computing,” Nature, 2017. DOI: https://doi.org/10.1038/nature23307
  • K. M. Kim and R. S. Williams, “A Family of Stateful Memristor Gates for Complete Cascading Logic,” IEEE Transactions on Circuits and Systems I, 2019. DOI: https://doi.org/10.1109/TCSI.2019.2926811
  • 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, “Analog signal and image processing with large memristor crossbars,” Nature Electronics, 2018. DOI: https://doi.org/10.1038/s41928-017-0002-z
  • 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, “Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing,” Nature Materials, 2017. DOI: https://doi.org/10.1038/nmat4756
  • M. D. Pickett, G. Medeiros-Ribeiro, and R. S. Williams, “A scalable neuristor built with Mott memristors,” Nature Materials, 2013. DOI: https://doi.org/10.1038/nmat3510
  • J. J. Yang, M. D. Pickett, X. Li, D. A. A. Ohlberg, D. R. Stewart, and R. S. Williams, “Memristive switching mechanism for metal/oxide/metal nanodevices,” Nature Nanotechnology, 2008. DOI: https://doi.org/10.1038/nnano.2008.160
  • D. B. Strukov, G. S. Snider, D. R. Stewart, and R. S. Williams, “The missing memristor found,” Nature, 2008. DOI: https://doi.org/10.1038/nature06932
  • J. R. Heath, P. J. Kuekes, G. S. Snider, and R. S. Williams, “A Defect-Tolerant Computer Architecture: Opportunities for Nanotechnology,” Science, 1998. DOI: https://doi.org/10.1126/science.280.5370.1716
  • J. L. Borghetti, G. S. Snider, P. J. Kuekes, J. J. Yang, D. R. Stewart, and R. S. Williams, “Memristive switches enable stateful logic operations via material implication,” Nature, 2010. DOI: https://doi.org/10.1038/nature08940