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HfOx/AlOySuperlattice‐Like Memristive Synapse
https://orcid.org/0000-0001-8335-2033
AbstractThe 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/AlOyfilm. The optimized SLL memristor achieves outstanding conductance modulation performance with linearly synaptic weight update (nonlinear factorα= 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 (≥ 104s 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/AlOySLL memristor provides a design viewpoint and optimization strategy for neuromorphic computing.
HfOx/AlOySuperlattice‐Like Memristive Synapse
https://orcid.org/0000-0001-8335-2033
AbstractThe 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/AlOyfilm. The optimized SLL memristor achieves outstanding conductance modulation performance with linearly synaptic weight update (nonlinear factorα= 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 (≥ 104s 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/AlOySLL memristor provides a design viewpoint and optimization strategy for neuromorphic computing.
HfOx/AlOySuperlattice‐Like Memristive Synapse
https://orcid.org/0000-0001-8335-2033
AbstractThe 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/AlOyfilm. The optimized SLL memristor achieves outstanding conductance modulation performance with linearly synaptic weight update (nonlinear factorα= 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 (≥ 104s 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/AlOySLL memristor provides a design viewpoint and optimization strategy for neuromorphic computing.
HfOx/AlOySuperlattice‐Like Memristive Synapse
Advanced Science
Wang, Chengxu (author) / Mao, Ge‐Qi (author) / Huang, Menghua (author) / Huang, Enming (author) / Zhang, Zichong (author) / Yuan, Junhui (author) / Cheng, Weiming (author) / Xue, Kan‐Hao (author) / Wang, Xingsheng (author) / Miao, Xiangshui (author)
Advanced Science ; 9
2022-07-01
Article (Journal)
Electronic Resource
English
The Memristive Magnetic Tunnel Junction as a Nanoscopic Synapse-Neuron System
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