Controlled Memory and Threshold Switching Behaviors in a Heterogeneous Memristor for Neuromorphic Computing
Hao‐Yang Li, Xiaodi Huang, Jun‐Hui Yuan, Yifan Lu, Tianqing Wan, Yi Li, Kan‐Hao Xue, Yuhui He, Ming Xu, Hao Tong, Xiangshui Miao
Abstract
Abstract The fully memristive neural network is emerging as a game‐changer in the artificial intelligence competition. Artificial synapses and neurons, as two fundamental elements for hardware neural networks, have been substantially implemented by different devices with memory and threshold switching (TS) behaviors, respectively. However, obtaining controllable memory and TS behaviors in the same memristive material system is still a considerable challenge that holds great potential for realizing compatible artificial neurons and synapses. Here, a heterogeneous bilayer conductive filamentary memristor comprising two different electrolytes with distinct copper ion mobility is reported: Cu/GeTe/Al 2 O 3 /Pt, which can demonstrate the governance of switching types. Experimentally, when the thickness of the Al 2 O 3 layer is 3 nm, stable nonvolatile multilevel memory switching (MS) is observed and employed to mimic the synaptic plasticity. With increasing oxide thickness, the switching behavior under the same compliance current alters from MS to volatile TS and is used to emulate the integrate‐and‐fire neuron function. The controllable switching stems from the change in the metal filament morphology within the Al 2 O 3 layer, which is supported by ab initio calculation results. This method enables a new pathway for constructing functionally reconfigurable neuromorphic devices for intelligence neuromorphic systems.