Image Denoising Based on Spin-Torque Diode with Injection Locking

Abstract

Spin-torque diodes based on magnetic tunnel junctions have emerged as a promising paradigm for microwave detection and neuromorphic computing due to their intrinsic nonlinear electrical characteristics. Traditional artificial intelligence image processing technology based on Von Neumann architecture faces the bottlenecks of power consumption and storage wall. Therefore, we propose and demonstrate hardware-inspired artificial neurons featuring a rectified linear unit (ReLU) activation function by leveraging injection-locked spin-torque diodes. Taking full advantage of the above properties, we construct a supervised autoencoder architecture for image denoising. The proposed architecture is validated on the MNIST dataset with additive Gaussian noise. Experimental results show that its denoising performance is on par with traditional software-implemented neural networks, featuring similar convergence performance and stable high-quality image reconstruction. Notably, even under heavy noise intensity of 0.3, the system consistently maintains a peak signal-to-noise ratio above 20 dB and a structural similarity index exceeding 0.85 across all scenarios. This study provides a viable hardware-native technical pathway toward edge artificial intelligence applications.