An Artificial L‐ReLU Neuron with Asymmetric Diffusive Memristor for High‐Accuracy Neuromorphic Systems

作者：Jianxun Zou, Zhe Feng, Zhibin Qian, Zuheng Wu, Jingqi Zheng, Lubo Gao, Ruotong Yang, Wenbin Guo, Zhihao Lin, Hao Yang, Haochen Wang, Hao Ruan, Zuyu Xu, Yunlai Zhu, Xumeng Zhang, Yuehua Dai · 发表于：Advanced Functional Materials · 年份：2025 · DOI：10.1002/adfm.202423267 · 被引用次数：8 · 研究领域：Advanced Memory and Neural Computing、Neuroscience and Neural Engineering、Neural dynamics and brain function

Abstract Artificial neurons with leaky rectified linear unit (L‐ReLU) function can effectively process negative information, enhancing the neuromorphic systems capbility to handle negative values. Memristive devices show great potential in building compact and bio‐plausible artificial neurons, however, a neuron device that supports L‐ReLU functions is still lacking. In this work, a compact L‐ReLU neuron is proposed based on a bipolar asymmetrical diffusive memristor. Utilizing intercalation and leveraging the migration diffusion ratio of Ag ions, devices meeting the characteristics of the L‐ReLU function are fabricated (Ag/TaO x /SiO x /Pt). Thus, the constructed neuron can encode the positive and negative input information into positive and negative spikes, respectively, with L‐ReLU‐like response function. In addition, to address the problem that the frequency only has positive values, a vector frequency (VF) is defined to describe the frequency of the positive and negative spikes. Moreover, a convolutional neural network and You‐Only‐Look‐Once version 3 (YOLOv3) network are constructed with the L‐ReLU neurons for gesture language recognition and target detection tasks, respectively. The network based on L‐ReLU neurons can avoid negative information squandering, showing a higher inference accuracy than the network with ReLU neurons. The results show that the neurons can offer a promising platform for building high‐accuracy neuromorphic systems.