Amorphous Boron Nitride Memristive Device for High-Density Memory and Neuromorphic Computing Applications

Abstract

Although two-dimensional (2D) nanomaterials are promising candidates for use in memory and synaptic devices owing to their unique physical, chemical, and electrical properties, the process compatibility, synthetic reliability, and cost-effectiveness of 2D materials must be enhanced. In this context, amorphous boron nitride (a-BN) has emerged as a potential material for future 2D nanoelectronics. Therefore, we explored the use of a-BN for multilevel resistive switching (MRS) and synaptic learning applications by fabricating a complementary metal-oxide-semiconductor (CMOS)-compatible Ag/a-BN/Pt memory device. The redox-active Ag and boron vacancies enhance the mixed electrochemical metallization and valence change conduction mechanism. The synthesized a-BN switching layer was characterized using several analyses. The fabricated memory devices exhibited bipolar resistive switching with low set and reset voltages (+0.8 and −2 V, respectively) and a small operating voltage distribution. In addition, the switching voltages of the device were modeled using a time-series analysis, for which the Holt’s exponential smoothing technique provided good modeling and prediction results. According to the analytical calculations, the fabricated Ag/a-BN/Pt device was found to be memristive, and its MRS ability was investigated by varying the compliance current. The multilevel states demonstrated a uniform resistance distribution with a high endurance of up to 104 direct current (DC) cycles and memory retention characteristics of over 106 s. Conductive atomic force microscopy was performed to clarify the resistive switching mechanism of the device, and the likely mixed electrochemical metallization and valence change mechanisms involved therein were discussed based on experimental results. The Ag/a-BN/Pt memristive devices mimicked potentiation/depression and spike-timing-dependent plasticity-based Hebbian-learning rules with a high pattern accuracy (90.8%) when implemented in neural network simulations.

Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications

“…In a recent study, Khot et al demonstrated high-density memory applications of the CBRAMs in a-BN-based devices ( Figure 9 a) [ 58 ]. The Ag/a-BN/Pt device presents multilevel switching achieved by controlling the compliance current during the SET process, as shown in Figure 9 b.…”

“…Reprinted from Ref. [ 58 ]. ( e ) The data retention time of 10 years at 177 °C was demonstrated by an Al 2 O 3 -based CBRAM device with Te as the AE material.…”

Ultrafine Nickel Sulfide-Based Bipolar Resistive Switching Device as Artificial Synapses for Neuromorphic Application

“…Nickel-based materials, particularly nickel oxides, have been widely investigated as a resistive switching material for high-density memory applications. − The resistive switching devices are considered as artificial synapses upon the application of low-power electrical stimuli . Reports have been published on boron nitride, cobalt phosphate, copper oxide, zinc oxide, tantalum oxide, and titanium dioxide nanotube-based devices for high-density memory and/or neuromorphic computing applications. − The nickel-based sulfide materials have wide applications in dye-sensitized solar cells, supercapacitors, rechargeable batteries, electrochemical sensors, oxygen evolution reactions, and photocatalysis. − However, the neuromorphic performance study of nickel sulfide-based materials is still unexplored. For this reason, we have considered nickel sulfide nanomaterials for neuromorphic computing applications by fabricating a metal–insulator–metal type of device.…”

Power‐Efficient and Highly Uniform BiFeO3‐Based Memristors Optimized with TiInSnO Electrode Interfacial Effect

“…Therefore, the demand for next‐generation non‐volatile memories (NVMs) has increased in both the consumer and industrial domains. [ 1 ] It is necessary to develop new device to realize the simultaneous storage and compute in one device to overcome von Neumann bottleneck. [ 2–4 ] Among the various types of NVMs, memristors, as an emerging and promising memory storage device, have attracted widespread attention due to their low cost, high density, long‐term data retention, fast operating speed, high endurance, and simple structure.…”