Uniform multilevel switching of graphene oxide-based RRAM achieved by embedding with gold nanoparticles for image pattern recognition

Abstract

Traditional metal-oxide semiconductor devices are inadequate for use in artificial neural networks (ANNs) owing to their high power consumption, complex structures, and difficult fabrication techniques. Resistive random access memory (RRAM) is a promising candidate for ANNs owing to its simple structure, low power consumption, and excellent compatibility with CMOS. Moreover, it can mimic synaptic functions because of its multilevel resistive switching (RS) behavior. Herein, we demonstrate highly uniform RS and a high on/off ratio of RRAM based on graphene oxide by embedding gold nanoparticles into the device. This allowed reliable multilevel storage. Further, multilevel RRAM based on spike-timing-dependent-plasticity learning rules was used for image pattern recognition. These findings may offer a route to develop reliable digital memristors for ANNs.

Transformation of digital to analog switching in TaOx-based memristor device for neuromorphic applications

“…Henceforth, Device B is found to be more appealing as an analog device for neuromorphic applications. 27,28 In order to evaluate its analogous behavior, we performed multilevel cell (MLC) characteristic test, and the result is shown in Fig. 2.…”

Improvement of Resistive Switching Performance in Sulfur-Doped HfOx-Based RRAM

“…uniformity, energy consumption and other performances [15,16,22]. There have rece lated studies on sulfur (S)-based resistive switching devices.…”

“…However, it is still difficult to comprehensively improve device performances; for example, its low energy consumption and high cycle-to-cycle uniformity. Researchers have proposed some solutions to solve the existing problems for RRAM: ion doping [ 9 , 12 , 15 , 16 ], double-layer structure [ 17 , 18 , 19 ], electrode engineering [ 20 , 21 ], etc. Among these methods, ion doping is considered an effective method to improve device uniformity, energy consumption and other performances [ 15 , 16 , 22 ].…”

Self-Powered Memristive Systems for Storage and Neuromorphic Computing

“…Research efforts have therefore been devoted to the development of energy-efficient memristive systems ( Tao et al, 2017 ; Zhao X. et al, 2017 ; Jang et al, 2018 ; Zidan et al, 2018 ; Choi et al, 2020 ; Lübben et al, 2020 ). Traditional approaches for achieving ultra-low-power memristors include optimizing the switching operation, for example reducing switching current/voltage, and increasing switching speed, and so forth ( Li Y. et al, 2018 ; Tao et al, 2018 ; Qi et al, 2020 ; Tian et al, 2020 ; Tseng et al, 2018 ).…”