Collaborative Research: Energy Efficient Voltage Controlled Non-volatile Domain Wall Devices for Neural Networks

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As Deep Neural Networks (DNNs) are increasingly deployed in low power embedded device and Internet of Things (IoT) applications. They need to be able to learn in real time while also being energy efficient. This necessitates the use of multi-state memory which is more than the conventional binary “0” and “1” states, is non-volatile such that information is retained when power is turned off, and can be programmed with very little energy. The goal of this project is to study and demonstrate synaptic elements of a neural network, which can store the weights updated during learning using voltage-controlled magnetic domain wall (DW) devices. Information is encoded as the position of a DW in a narrow magnetic wire. Specifically, the research will focus on using the strain generated by application of a small voltage to a thin piezoelectric layer and transferred to a magnetic wire deposited on it to control DW position in an extremely energy efficient manner. This research could lead to a dense, energy efficient and robust hardware paradigm for implementing DNNs. Two graduate students, one at Virginia Commonwealth University (VCU) and one at Massachusetts Institute of Technology (MIT), will gain multidisciplinary skills in advanced nanofabrication, nano-characterization and modeling. The VCU-PI and MIT- Co-PI will incorporate domain wall technology for memory and computing in the courses they teach. The PI and Co-PI plan to host research interns in their labs recruited from outreach programs for underrepresented groups in their respective universities. The students will be trained on nanofabrication of nanomagnets and other aspects of magnetic technology. The PI and Co-PI also plans to hold nanomagnetism workshops for high school students and teachers in their Universities collaboratively. This collaborative effort between VCU and MIT work will study and demonstrate the use of racetracks comprised of magnetostrictive metals such as CoFe, where DWs are moved using Spin Orbit Torque (SOT) from an adjoining Pt layer and arrested deterministically using voltage generated strain from a piezoelectric layer underneath that modulate perpendicular magnetic anisotropy (PMA) in different regions of a racetrack. The research team further plan to explore the use of magnetostrictive Rare Earth Iron Garnets (REIG) that have lower saturation magnetization and low damping, allowing for lower SOT applied for lesser time due to large DW velocities in order to improve the energy efficiency of DW devices. The proposed work will consist of complementary materials growth, characterization, nanofabrication, advanced magnetic visualization, modeling and simulation that includes: (i) Growth of metallic ferromagnetic and insulating ferrimagnets (ii) Study of SOT-driven DW velocity in magnetostrictive racetracks and proof-of-concept demonstration of arresting SOT-driven DW motion with a voltage induced strain (iii) Performing micromagnetic modeling of domain wall motion with SOT and its control with voltage-induced strain in the presence of notches, edge effects and room temperature thermal noise and evaluating the overall performance benefit of the proposed device in implementing DNNs. The research in this project will advance the knowledge of DW dynamics under local voltage- induced variations in anisotropy, in heterostructures that exhibit rich physics of SOT and the presence of chiral DWs. It will also provide a proof-of-concept demonstration of synaptic and neuron devices that could pave the way for energy-efficient hardware implementation of DNNs.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.