Intellectual merit: This project is awarded under the Nanoelectronics for 2020 and Beyond competition, with support by multiple Directorates and Divisions at the National Science Foundation as well as by the Nanoelectronics Research Initiative of the Semiconductor Research Corporation. The complementary metal oxide semiconductor field effect transistor, considered the workhorse of modern computing machinery, is inherently energy-inefficient because it is a charge-based digital switch. In contrast, a single-domain nanomagnet with uniaxial shape anisotropy, that encodes binary bits in its magnetization orientation, is much more energy-efficient because it is a spin-based switch in which the spins internally interact. Therefore, magnetic computing circuits hold a potential advantage over their electronic counterparts. That advantage however will be lost if the methodology used to switch the magnet becomes so energy-inefficient that it adds an exorbitant energy overhead. To this end, a hybrid spintronic/straintronic paradigm for switching magnets has been developed that reduces the energy dissipation by several orders of magnitude and heralds an ultra-energy-efficient magnetic computing and signal processing architecture. This project will: (1) develop all the modeling tools necessary to simulate these devices and their switching dynamics. They will incorporate the effects of device and circuit stochasticity and thermal fluctuations via appropriate models such as stochastic Landau-Lifshitz-Gilbert equations and/or Fokker-Planck equations; (2) demonstrate Bennett clocking and successful logic bit propagation in a digital gate array fabricated with nanolithography, where clocking is carried out with tiny voltages generating strain; (3) design energy-efficient neuromorphic architectures based on multi-state hybrid spintronic/straintronic synapses and neurons that can process analog signals; and (4) demonstrate image processing with straintronic/spintronic nodes communicating via spin waves to implement specific image morphing algorithms. These image processors will be extremely fast since they will rely on the physics of magnetic interactions between spin wave circuits and the collective activity of multiferroic magnetic cells to elicit the required functionality, without requiring any software or execution of instruction sets.

Broader Impact: The proposed research will potentially impact all areas of computing and signal processing. Computers employing the hybrid spintronics/straintronics approach can be so energy-efficient that they could operate by harvesting energy from the surroundings, without requiring a battery. Thus, they have unprecedented applications in medical devices implanted in an epileptic patient?s brain to monitor brain signals and warn of an impending seizure. They can run by harvesting energy from the patient?s body motion alone. They also have other applications in areas such as structural health monitoring where they can constantly monitor fatigue and fracture propagation in bridges and buildings, while harvesting energy from vibrations induced by wind or passing traffic. Integration of this research with education will entail traditional graduate and undergraduate student training, while minority enrichment will involve training high-school students recruited through the Richmond Area Program for Minorities in Engineering at Virginia Commonwealth University, minority outreach centers at University of California-Riverside, Office of Engineering Outreach and Engagement at Michigan, and the Center for Diversity in Engineering at University of Virginia. K-12 outreach will leverage the Math and Science Innovation Center at Richmond and the Summer Discovery Program at Virginia Commonwealth University. New graduate course material will be developed at each participating institution and disseminated through textbooks, tutorials and the worldwide web.

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Virginia Commonwealth University
United States
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