CAREER: Toward energy-efficient bio-inspired magnonic processing with nanomagnetic arrays

University Of Delaware, Newark DE

Investigators

Abstract

This project is jointly funded by the Condensed Matter Physics program of the Division of Materials Research and Established Program to Stimulate Competitive Research (EPSCoR). Nontechnical description: The surging development of artificial intelligence (AI) enables the creation of powerful tools and applications that were unimaginable just a few years ago. However, as AI and machine learning rapidly grow, the associated energy costs and greenhouse emissions are exploding. This massively unsustainable trend threatens to prevent society from achieving a net-zero future. Hence, a paradigm shift for low-power computing and AI processing is urgently needed. This project contributes to tackling this historic challenge by delivering foundational knowledge and technology concerning the fundamental excitations in magnetic nanostructures to create a transformative computing scheme taking inspiration from the brain. Current computing architectures rely on a constant shuttling of data between separate memory and processor, which is highly inefficient. Furthermore, current computing platforms are based on the flow of electronic charges, leading to dissipation in the form of Joule heating. To circumvent these problems, the research team aims to harness the dynamics in networks of interacting nanomagnets for bio-inspired processing by A) alleviating the processor-memory information transfer bottleneck and B) enabling the transport and processing of data based on waves rather than moving charges. The educational outreach component of this project fosters increased public participation in scientific research. The educational goals are designed to engage multiple levels of learning in wave physics: 1) a new course is developed for lifelong learners and 2) training programs are developed for schoolteachers by creating an accessible wave demonstration. Technical description: Spin waves, and their quanta - magnons - are the fundamental collective excitations of a magnetic system. Magnons can transport and process information without moving charges, and hence, magnonic devices can be less dissipative than their electronic counterparts. Nanomagnetic arrays are similar to neural networks, providing memory and computing abilities in the same unit: they can retain information stored in their magnetization orientation and process that information by magnonic excitations. This project explores several paths in nanomagnonics by determining the magnon properties in lithographically defined arrays of interacting nanomagnets, where information is passed between nanomagnetic ‘neurons’ via magnon-magnon coupling acting as ‘synapses’. Therefore, advances are needed to understand dynamic mode coupling in networks of nanomagnets. This project addresses critical knowledge gaps in the fundamental understanding of strongly interacting magnetic networks. The four specific aims are 1) controlling magnons in two-dimensional arrays of nanomagnets, 2) manipulating magnon-magnon interactions, and 3) understand nonlinear dynamics in magnetic nanostructures to 4) experimentally realize the next-generation of neuromorphic magnonic computing concepts. The nanomagnetic networks are fabricated by electron-beam lithography, electron-beam evaporation, and lift-off and studied by optical, electrical, and microwave methods. The experimental investigations are supported by micromagnetic modeling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.