Cheng Zhang, Qinan Wang, Chun Zhao, Huanjun Lu, Chao Li, Kuaibing Wang, Xiaowei Wang, Yinxiao Li, Yinxiao Li, Fangchao Li, Fuqin Sun, Lin Liu, Yingyi Wang, Kejie Guan, Zhongrui Wang, Lixing Kang, Wan Qian, Yang Li, Yang Li, Yang Li, Ting Zhang
ABSTRACT The low‐power ionic‐type memristor and brain‐inspired neuromorphic device offer significant potential in breaking the power consumption wall. However, the precise control of uniform metallic conductive filament (CF) at both intra‐ and inter‐molecular levels rather than random migration raises a pressing challenge. Here, we first report a symmetrical dual‐core naphthalene diimide (bis‐NDI) molecular material featuring multi‐active and lamellarly ordered redox sites, which actuates reconfigurable analog‐to‐digital (A‐t‐D) memristive operations via the controllable manipulation of CF growth at the molecular scale. The bis‐NDI‐based memristor exhibits highly efficient analog synaptic behaviors, demonstrating an ultralow‐power consumption of 90 aJ µm −2 . By effectively re‐organizing lamellar redox sites, the device dynamically implements A‐t‐D transition with an operating voltage of 0.5 V (lower than most reported organic memristors) and ultrahigh yield of 98%. Relying on the bis‐NDI induced A‐t‐D dynamic plasticity, a novel feedback mechanism of pruning algorithm is subtly devised for granular error analysis and voltage adjustment validation in spiking neural networks (SNNs) computing. The co‐design of material‐algorithm can effectively reduce the number of connected neurons (max reduced proportion = 92%), thereby achieving ultralow systemic energy consumption while maintaining exalted recognition rates (>90%). This work paves the material‐algorithm cooperation way to realize ultralow‐power neuromorphic devices and highly‐efficient spiking computing.