Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

William Borders (Fed)

NRC Postdoctoral Fellow

William Borders is an NRC Postdoctoral Fellow in the Alternative Computing Group in the Nanoscale Device Characterization Division of the Physical Measurement Laboratory (PML). He received a B.S. in electrical and computer engineering from North Carolina State University and an M.S. and Ph.D. in electronic engineering from Tohoku University in Japan. For his doctoral research, he studied stochastic magnetic tunnel junctions and their use as a key component in probabilistic computing-based applications. His current work focuses on demonstrating intermediate scale neural network hardware implementing novel devices for next-gen AI applications.

Selected Publications

  • Sigmoidal curves of stochastic magnetic tunnel junctions with perpendicular easy axis, Kobayashi, K., Borders, W. A., Kanai, S., Hayakawa, K., Ohno, H., and Fukami, S., Applied Physics Letters 119, 132406 (2021).
  • Integer factorization using stochastic magnetic tunnel junctions, Borders, W. A., Pervaiz, A. Z, Fukami, S., Camsari, K. Y., Ohno, H., and Datta, S., Nature 573, 390-393 (2019).
  • Analogue spin-orbit torque device for artificial-neural-network-based associative memory operation, Borders, W. A., Akima, H., Fukami, S., Moriya, S., Kurihara, S., Horio, Y., Sato, S., and Ohno, H., Applied Physics Express 10, 013007 (2017


Programmable electrical coupling between stochastic magnetic tunnel junctions

Sidra Gibeault, Temitayo Adeyeye, Liam Pocher, Daniel Lathrop, Matthew Daniels, Mark Stiles, Jabez J. McClelland, William Borders, Jason Ryan, Philippe Talatchian, Ursula Ebels, Advait Madhavan
Superparamagnetic tunnel junctions (SMTJs) are promising sources of randomness for compact and energy efficient implementations of various probabilistic

Neural networks three ways: unlocking novel computing schemes using magnetic tunnel junction stochasticity

Matthew Daniels, William Borders, Nitin Prasad, Advait Madhavan, Sidra Gibeault, Temitayo Adeyeye, Liam Pocher, Lei Wan, Michael Tran, Jordan Katine, Daniel Lathrop, Brian Hoskins, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland
Due to their interesting physical properties, myriad operational regimes, small size, and industrial fabrication maturity, magnetic tunnel junctions are
Created June 25, 2021, Updated December 9, 2022