Time-Dependent Operations in Molecular Gap Atomic Switches

Ayana Suzuki, Tohru Tsuruoka, Tsuyoshi Hasegawa*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Learning by human beings is achieved by changing the synaptic weights of a neural network in the brain. Low frequency stimulation temporarily increases a synaptic weight, which then decreases to the initial low state in the interval after each stimulation. Conversely, high frequency stimulation keeps a synaptic weight at an elevated level, even after the stimulation ends. These phenomena are termed short-term plasticity (STP) and long-term potentiation (LTP), respectively. These functions have been emulated by various nonvolatile devices, with the aim of developing hardware-based artificial intelligent (AI) systems. In order to use the functions in actual AI systems with other conventional devices, control of the operating characteristics, such as matching a decay constant in STP, is indispensable. This paper reports an electrochemical method for controlling the characteristics of time-dependent neuromorphic operations of molecular gap atomic switches. Pre-doping of Ag+ cations into an ionic transfer layer (Ta2O5) changes the amount of shift in an electrochemical potential in the time-dependent operation, which drastically improves the decaying characteristics in STP mode.

Original languageEnglish
Article number1900068
JournalPhysica Status Solidi (B) Basic Research
Issue number8
Publication statusPublished - 2019 Aug


  • electrochemical potential
  • long-term potentiation
  • neuromorphic operation
  • short-term plasticity
  • synapses

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics


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