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Ni Single-Atoms Based Memristors with Ultrafast Speed and Ultralong Data Retention.

Hua-Xin LiQing-Xiu LiFu-Zhi LiJia-Peng LiuGuo-Dong GongYu-Qi ZhangYan-Bing LengTao SunYe ZhouSu-Ting Han
Published in: Advanced materials (Deerfield Beach, Fla.) (2023)
Memristor with low-power, high density and scalability fulfills the requirements of the applications of the new computing system beyond Moore's law. However, there are still nonideal device characteristics observed in the memristor to be solved. The important observation is that retention and speed are correlated parameters of memristor with trade off against each other. The delicately modulating distribution and trapping level of defects in electron migration based memristor is expected to provide compromise method to address the contradictory issue of improving both switching speed and retention capability. Here, we report high performance memristor based on structure of ITO/Ni single-atoms (NiSAs/N-C)/Polyvinyl pyrrolidone (PVP)/Au. By utilizing well-distributed trapping site with reasonable trapping depth, small tunnelling barriers/distance and high charging energy, the memristor with an ultrafast switching speed of 100 ns, extremely long retention capability of 10 6 s, a low set voltage (V set ) of ∼0.7 V, a substantial ON/OFF ration of 10 3 , and a low spatial variation in cycle-to-cycle (500 cycles) and device-to-device characteristics (128 devices) has been demonstrated. On the premise of preserving the strengths of a fast switching speed, this memristor exhibits ultralong retention capability comparable to the commercialized flash memory. According to density functional theory (DFT) calculations of the charge distribution surrounding each Ni atom, single-atom space-limited effect of NiSAs/N-C with special structure of Ni-N 4 has been confirmed. Finally, we have further implemented a memristor ratioed logic (MRL) based combinational memristor array including AND, OR, and XOR logic gate to realize the one-bit full adder. This article is protected by copyright. All rights reserved.
Keyphrases
  • density functional theory
  • molecular dynamics
  • high density
  • electron transfer
  • high resolution
  • molecular dynamics simulations
  • deep learning
  • sensitive detection
  • big data
  • mass spectrometry
  • neural network