All‐Electrical Control of Spin Synapses for Neuromorphic Computing: Bridging Multi‐State Memory with Quantization for Efficient Neural Networks - Hsin, Tzu‐Chuan; Lin, Chun‐Yi; Wang, Po‐Chuan; Yang, Chun; Pai, Chi‐Feng (Advanced science, 2025)
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. This study develops three all‐electrically controlled, field‐free spintronic synapse devices for neuromorphic computing. The tilted anisotropy device achieves an 11‐state memory with minimal cycle‐to‐cycle variation (2%), enabling high‐accuracy neural network quantization (81.51% in ResNet‐18). These findings position spintronic synapses as a promising solution for energy‐efficient AI hardware.