Self‐Assembled Networked PbS Distribution Quantum Dots for Resistive Switching and Artificial Synapse Performance Boost of Memristors

Abstract

Resistive switching (RS) is a promising emerging storage technology that has received much attention due to its many advantages, such as economy, fast operating speed, long retention, high density, and low energy consumption. [1] RS effects are widely applied in the fields of nonvolatile RS random access memory (RRAM), artificial neural computing, and reconfigurable logic operations and so on. [2] RRAM memories based on electrochemical metallization (ECM) and valance change mechanism (VCM) are commonly used for the memristor application. [3] Compared with conventional computing based on the von Neumann architecture, memristor (i.e., RS device) computing is proving to be superior for brain-inspired computing, such as image processing and speech recognition, [2] where the diffusion of the metal ions such as Ag + , Cu + , or oxygen vacancies are used to mimic the diffusion of Ca + in the neural cell. [4] However, the switching voltages in the memristor devices (MDs) showWith the advent of the era of big data, resistive random access memory (RRAM) has become one of the most promising nanoscale memristor devices (MDs) for storing huge amounts of information. However, the switching voltage of the RRAM MDs shows a very broad distribution due to the random formation of the conductive filaments. Here, selfassembled lead sulfide (PbS) quantum dots (QDs) are used to improve the uniformity of switching parameters of RRAM, which is very simple comparing with other methods. The resistive switching (RS) properties of the MD with the self-assembled PbS QDs exhibit better performance than those of MDs with pure-Ga 2 O 3 and randomly distributed PbS QDs, such as a reduced threshold voltage, uniformly distributed SET and RESET voltages, robust retention, fast response time, and low power consumption. This enhanced performance may be attributed to the ordered arrangement of the PbS QDs in the self-assembled PbS QDs which can efficiently guide the growth direction for the conducting filaments. Moreover, biosynaptic functions and plasticity, are implemented successfully in the MD with the self-assembled PbS QDs. This work offers a new method of improving memristor performance, which can significantly expand existing applications and facilitate the development of artificial neural systems. Data Storage

The Design of 3D‐Interface Architecture in an Ultralow‐Power, Electrospun Single‐Fiber Synaptic Transistor for Neuromorphic Computing

“…Nowadays, various types of electronic devices have been developed to simulate synaptic properties, such as phase‐change memories,13 synaptic transistors,14–16 and memristors 17–19. Synaptic behaviors have been successfully mimicked by these devices.…”

A Pure 2H‐MoS2 Nanosheet‐Based Memristor with Low Power Consumption and Linear Multilevel Storage for Artificial Synapse Emulator

“…However, the barrier of CMOS synapses has appeared due to its large area circuit and high‐power consumption 1. Nowadays, memristor has been demonstrated which could be potentially used to emulate biological synapses due to the remarkable data processing capabilities of memristors and their similarity to biological synapses, as well as breaking the limitations of von Neumann architecture 2–5. Although memristors have great potential in brain‐like computing and artificial intelligence, but their electrical properties, power consumption, linear conductance modulation, and other characteristics require further improvement 6–8…”

Overview of Resistive Random Access Memory (RRAM): Materials, Filament Mechanisms, Performance Optimization, and Prospects

“…Copyright 2010, American Chemical Society. g–i) Reproduced with permission . Copyright 2018, Wiley‐VCH.…”

“…Yan et al used a low‐dimensional material of lead sulfide (PbS) as a functional material of RRAMs and systematically studied the electrical properties as shown in Figure h,i . The self‐assembled PbS, which can efficiently guide the growth direction for the CFs (Figure g), leads to enhanced performance such as reduced threshold voltage, uniformly distributed SET/RESET voltages, fast response time, and low power consumption.…”