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

Xiaobing YanYifei PeiHuawei ChenJianhui ZhaoZhenyu ZhouHong WangLei ZhangJingjuan WangXiaoyan LiCuiya QinGong WangZuoao XiaoQianlong ZhaoKaiyang WangHui LiDeliang RenQi LiuHao ZhouJingsheng ChenPeng Zhou
Published in: Advanced materials (Deerfield Beach, Fla.) (2018)
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-Ga2 O3 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.
Keyphrases
  • quantum dots
  • big data
  • artificial intelligence
  • molecular dynamics
  • neural network
  • sensitive detection
  • working memory
  • pet ct
  • gold nanoparticles
  • deep learning
  • social media
  • high resolution