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论文类型：期刊论文

第一作者：王成旭,毛格齐

通讯作者：王兴晟

合写作者：黄梦华,黄恩铭,张子冲,袁俊辉,程伟明,薛堪豪,缪向水

发表刊物：Advanced Science

收录刊物：SCI、EI

所属单位：华中科技大学

学科门类：工学

一级学科：电子科学与技术

文献类型：J

页面范围：2201446

ISSN号：2198-3844

关键字：analog switching, conductive filaments, memristive synaptic device, neuromorphic computing, superlattice-like

DOI码：10.1002/advs.202201446

发表时间：2022-05-29

影响因子：17.521

摘要：The adjustable conductance of a two-terminal memristor in a crossbar array can facilitate vector-matrix multiplication in one step, making the memristor a promising synapse for efficiently implementing neuromorphic computing. To achieve controllable and gradual switching of multi-level conductance, important for neuromorphic computing, a theoretical design of a superlattice-like (SLL) structure switching layer for the multi-level memristor is proposed and validated, refining the growth of conductive filaments (CFs) and preventing CFs from the abrupt formation and rupture. Ti/(HfOx/AlOy)SLL/TiN memristors are shown with transmission electron microscopy , X-ray photoelectron spectroscopy , and ab initio calculation findings corroborate the SLL structure of HfOx/AlOy film. The optimized SLL memristor achieves outstanding conductance modulation performance with linearly synaptic weight update (nonlinear factor α = 1.06), and the convolutional neural network based on the SLL memristive synapse improves the handwritten digit recognition accuracy to 94.95%. Meanwhile, this improved synaptic device has a fast operating speed (30 ns), a long data retention time (≥ 104 s at 85 ℃), scalability, and CMOS process compatibility. Finally, its physical nature is explored and the CF evolution process is characterized using nudged elastic band calculations and the conduction mechanism fitting. In this work, as an example the HfOx/AlOy SLL memristor provides a design viewpoint and optimization strategy for neuromorphic computing.

发布期刊链接：https://onlinelibrary.wiley.com/doi/10.1002/advs.202201446