Facile synthesis of nickel cobaltite quasi-hexagonal nanosheets for multilevel resistive switching and synaptic learning applications

Abstract

High-density memory devices are essential to sustain growth in information technology (IT). Furthermore, brain-inspired computing devices are the future of IT businesses such as artificial intelligence, deep learning, and big data. Herein, we propose a facile and hierarchical nickel cobaltite (NCO) quasi-hexagonal nanosheet-based memristive device for multilevel resistive switching (RS) and synaptic learning applications. Electrical measurements of the Pt/NCO/Pt device show the electroforming free pinched hysteresis loops at different voltages, suggesting the multilevel RS capability of the device. The detailed memristive properties of the device were calculated using the time-dependent current–voltage data. The two-valued charge-flux properties indicate the memristive and multilevel RS characteristics of the device. Interestingly, the Pt/NCO/Pt memristive device shows a compliance current (CC)-dependent RS property; compliance-free RS was observed from 10 −2 to 10 −4 \u2009A, and the compliance effect dominated in the range of 10 −5 –10 −6 \u2009A. In CC control mode, the device demonstrated three resistance states during endurance and retention measurements. In addition, the device was successful in mimicking biological synaptic properties such as potentiation-depression- and spike-timing-dependent plasticity rules. The results of the present investigation demonstrated that solution-processable NCO nanosheets are potential switching materials for high-density memory and brain-inspired computing applications. A simple technique for producing nanostructured oxides shows promise for developing neuromorphic computing systems. Tae Geun Kim from Korea University in Seoul, South Korea, and co-workers report that nickel cobaltite, a low-cost material being considered for non-volatile memory devices, can be synthesized as porous nanosheets through a co-precipitation method. The team demonstrated that when stacked a few hundred nanometers thick, these sheets had properties ideal for ‘resistive switching’, an effect that stores data by transforming insulators into conductors using metal filaments. Characterizations revealed that the material’s morphology proved ideal for fine-tuning filament growth, enabling the memory cell to switch between three conductive states. Because the nanosheet devices retained memories of their switching history, they were capable of mimicking neural network behavior, such as the potentiation, depression and spike-timing-dependent plasticity processes associated with synapses. A resistive switching device is fabricated using nanostructured nickel cobaltite for high-density data storage and synaptic learning applications. The active switching layer of the device consists of quasi-hexagonal porous nanosheets that enable smooth charge transport. The device shows voltage tunable and forming-free resistive switching effect and non-ideal memristive properties. The rational design of the device helps to show controlled multilevel resistive switching property and thereby switch between three conductive states. The analog switching of the device helps to mimic specific neural network behavior, such as the potentiation, depression and four spike-timing-dependent plasticity rules.