All‐Electrical Control of Spin Synapses for Neuromorphic Computing: Bridging Multi‐State Memory with Quantization for Efficient Neural Networks

The development of energy-efficient, brain-inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve high accuracy and adaptability. In this study, three types of all-electrically controlled, field-free spin synapse devices designed with unique spintronic structures presented: the Néel orange-peel effect, interlayer Dzyaloshinskii-Moriya interaction (i-DMI), and tilted anisotropy. To systematically evaluate their neuromorphic potential, a benchmarking framework is introduced that characterizes cycle-to-cycle (CTC) variation, a critical factor for reliable synaptic weight updates. Among these designs, the tilted anisotropy device achieves an 11-state memory with minimal CTC variation (2%), making it particularly suited for complex synaptic emulation. Through comprehensive benchmarking, this multi-state device in convolutional neural networks (CNNs) using post-training quantization is implemented. Results indicate that per-channel quantization, particularly with the min-max and mean squared error (MSE) observers, enhances classification accuracy on the CIFAR-10 dataset, achieving up to 81.51% and 81.12% in ResNet-18-values that closely approach the baseline accuracy. This evaluation underscores the potential of field-free spintronic synapses in neuromorphic architectures, offering an area-efficient solution that integrates multi-state functionality with robust switching performance. The findings highlight the promise of these devices in advancing neuromorphic computing, contributing to energy-efficient, high-performance systems inspired by neural processes.