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Self‐Assembled Networked PbS Distribution Quantum Dots for Resistive Switching and Artificial Synapse Performance Boost of Memristors
Author(s) -
Yan Xiaobing,
Pei Yifei,
Chen Huawei,
Zhao Jianhui,
Zhou Zhenyu,
Wang Hong,
Zhang Lei,
Wang Jingjuan,
Li Xiaoyan,
Qin Cuiya,
Wang Gong,
Xiao Zuoao,
Zhao Qianlong,
Wang Kaiyang,
Li Hui,
Ren Deliang,
Liu Qi,
Zhou Hao,
Chen Jingsheng,
Zhou Peng
Publication year - 2019
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201805284
Subject(s) - resistive random access memory , memristor , materials science , quantum dot , nanotechnology , optoelectronics , nanoscopic scale , voltage , electronic engineering , electrical engineering , engineering
With 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, self‐assembled 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.