Brain‐Inspired In‐Memory Data Pruning and Computing with TaO x Mem‐Selectors

作者：Yi Li, Jinru Lai, Songqi Wang, Ning Lin, Xu Zheng, Wenxuan Sun, Danian Dong, Xiqing Xu, Xiqing Xu, Haili Ma, Feng Zhang, Xiaojuan Qi, Zhongrui Wang, Xiaoxin Xu, Xiaoxin Xu, D. S. Shang, Han Wang, Ming Liu · 发表于：Advanced Materials · 年份：2025 · DOI：10.1002/adma.202502168 · 被引用次数：8 · 研究领域：Advanced Memory and Neural Computing、Ferroelectric and Negative Capacitance Devices、Neuroscience and Neural Engineering

Abstract The selective attention mechanisms inherent in the human visual system provide a promising framework for developing edge systems that can simultaneously prune and process critical information from visual input. However, conventional complementary metal‐oxide‐semiconductor‐based edge vision systems rely on complex digital logic for data pruning, alongside the physical separation of pruning, memory, and processing. This increases both power consumption and latency. Herein, a Mem‐Selector (M‐S) device that features reconfigurable non‐volatile resistive memory and volatile threshold switching in a Ta/TaO x /Ta 2 O 5 stack is presented. For the first time, using transmission electron microscopy, the formation and rupture of conductive oxygen vacancy filaments are observed when the device operates as a resistive memory, as well as the growth of Ta‐rich nanocrystalline clusters when it switches to threshold mode. This suggests the coexistence of ionic and electronic switching mechanisms. By leveraging a multifunctional M‐S device, an in‐memory pruning‐computing (IMPC) system that simultaneously prunes and processes information is constructed. The IMPC system, inspired by human visual‐selective attention, the IMPC system adaptively extracts essential information while pruning trivial inputs based on task complexity. This approach optimizes the balance between hardware cost and classification performance. Compared to conventional in‐memory computing systems, the integrated IM...