Hsin, Tzu‐ChuanTzu‐ChuanHsinLin, Chun‐YiChun‐YiLinWang, Po‐ChuanPo‐ChuanWangYang, ChunChunYangCHI-FENG PAI2025-06-172025-06-172025https://www.scopus.com/record/display.uri?eid=2-s2.0-105003819552&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/730129The 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.field-free switchingneural networkneuromorphic computingperpendicular magnetic anisotropyspin-orbit torques[SDGs]SDG7journal article10.1002/advs.202417735