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 A, and the compliance effect dominated in the range of 10−5–10−6 A. 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.

Versatile memristor for memory and neuromorphic computing

“…Volatile/non-volatile bifunctional memristors with one or two functions have been studied, as shown in Table 1. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] However, versatile memristors covering multiple functions, such as non-volatile memory, selectors, artificial neurons, and artificial synapses have not been investigated. It is difficult to guarantee large storage windows (both volatile and non-volatile models), excellent endurance, and multiple functions simultaneously.…”

Bioresistive Random-Access Memory with Gold Nanoparticles that Generate the Coulomb Blocking Effect Can Realize Multilevel Data Storage and Synapse Simulation

“…With the development of the big data era, some technologies (artificial intelligence, machine learning, etc.) require high-density storage devices to store and read the data requested in the application. , Silicon-based “flash” memory is undoubtedly the most successful nonvolatile memory at present, but people’s demand for storage density continues to increase, and the miniaturization of devices is becoming increasingly difficult. The size of the device is approaching the physical limit, so it is necessary to develop a new type of nonvolatile memory with high performance and low power consumption. − Resistive random access memory (RRAM) has attracted widespread attention in recent years because of its fast read and write speed, simple structure, and low power consumption. − It can store data by not only the high resistance state (HRS) and the low resistance state (LRS) but also the intermediate states between HRS and LRS; furthermore, RRAM can also be used for other functions, such as multilevel data storage and simulation of biological synapses. − A variety of materials have been reported to exhibit memory behavior, such as metal oxides, − organic polymers, perovskite materials, and so on.…”

“…require high-density storage devices to store and read the data requested in the application. 1,2 Silicon-based "flash" memory is undoubtedly the most successful nonvolatile memory at present, but people's demand for storage density continues to increase, and the miniaturization of devices is becoming increasingly difficult. The size of the device is approaching the physical limit, so it is necessary to develop a new type of nonvolatile memory with high performance and low power consumption.…”

Estudio de la conmutación resistiva a partir de la caracterización eléctrica de estructuras MIM

“…2. 19). Para evaluar mejor la ventana entre estados de resistencia, muchas veces se normalizan los valores de corriente considerando el valor 1 en el LRS.…”

“…Además, poseen prometedoras propiedades a la hora de poder sustituir a memorias como la flash gracias a su buena escalabilidad, gran rapidez de operación, y voltajes de operación pequeños, lo que las convierte en una tecnología de bajo consumo. Por otro lado, los dispositivos RRAM podrían ser ideales para crear circuitos neuromórficos [19,20], esto es, circuitos informáticos que simulan una red neuronal [21]. Las memorias resistivas tienen la propiedad de que, además de poder conmutar entre dos estados de resistencia, también pueden almacenar varios estados intermedios entre los dos principales, lo cual les permite imitar la sinapsis biólogica regulando la conexión entre las "neuronas" (los diferentes elementos electrónicos) del circuito [22] (Fig.…”