Gradient-based optimization of spintronic devices

Abstract

The optimization of physical parameters serves various purposes in device development, including in system identification and efficiency. Spin-torque oscillators have been experimentally and theoretically applied to neuromorphic computing, but their physical parameters are usually optimized via grid search procedures. In this paper, we propose a scheme to optimize the dynamics parameters of macrospin-type spin-torque oscillators using the gradient descent method with automatic differentiation. First, we numerically create dynamic data for teaching and tune the parameters to reproduce the dynamics. This approach can be applied to determine the correspondence between spin-torque oscillator simulations and experiments. Next, we solve an image recognition task with high accuracy by connecting a coupled system of spin-torque oscillators to the input and output layers and training all of them through gradient descent. Combining this approach with experimentation makes it possible to design an experimental setup and physical system to solve a task with high precision using a spin-torque oscillator.