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.

Matthew Daniels (Fed)

Matthew Daniels is a staff physicist in the Alternative Computing Group in the Nanoscale Device Characterization Division of the Physical Measurement Laboratory (PML). He received a B.S. in physics from Clemson University and a Ph.D. in physics from Carnegie Mellon University. For his doctoral research, he developed a semiclassical formalism for exposing a novel, spin-like degree of freedom in antiferromagnetic spin waves. Matthew is working on theoretical models for neuromorphic computing with spintronic devices and on using physics to understand, develop, and quantify energy-efficient computing schemes, information encodings, and coprocessors.

Selected Publications

  • Spin-transfer torque induced spin waves in antiferromagnetic insulators, M. W. Daniels, W. Guo, G. M. Stocks, D. Xiao, and J. Xiao, New Journal of Physics 17, 103039 (2015).
  • Antiferromagnetic Spin Wave Field-Effect Transistor, R. Cheng, M. W. Daniels, J.-G. Zhu, and D. Xiao, Scientific Reports 6, 24223 (2016).
  • Ultrafast switching of antiferromagnets via spin-transfer torque, R. Cheng, M. W. Daniels, J.-G. Zhu, and D. Xiao, Physical Review B 91, 064423 (2015).


Device Modeling Bias in ReRAM-Based Neural Network Simulations

Imtiaz Hossen, Matthew Daniels, Martin Lueker-Boden, Andrew Dienstfrey, Gina Adam, Osama Yousuf
The study of resistive-RAM (ReRAM) devices for energy efficient machine learning accelerators requires fast and robust simulation frameworks that incorporate

Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions

Jonathan Goodwill, Nitin Prasad, Brian Hoskins, Matthew Daniels, Advait Madhavan, Lei Wan, Tiffany Santos, Michael Tran, Jordan Katine, Patrick Braganca, Mark Stiles, Jabez J. McClelland
The increasing scale of neural networks and their growing application space have produced a demand for more energy and memory efficient artificial-intelligence

A System for Validating Resistive Neural Network Prototypes

Brian Hoskins, Mitchell Fream, Matthew Daniels, Jonathan Goodwill, Advait Madhavan, Jabez J. McClelland, Osama Yousuf, Gina C. Adam, Wen Ma, Muqing Liu, Rasmus Madsen, Martin Lueker-Boden
Building prototypes of heterogeneous hardware systems based on emerging electronic, magnetic, and photonic devices is an increasingly important area of research

Patents (2018-Present)


NIST Inventors
Advait Madhavan , Mark D. Stiles and Matthew Daniels
patent description The invention is a circuit/computer architecture that supports the rapid, energy efficient evaluation of tropical algebra primitives. These operations that choose the minimum or maximum elements among those in an array; operations that add together two values; operations that
Created July 30, 2019, Updated December 9, 2022