SPICE Study of STDP Characteristics in a Drift and Diffusive Memristor-Based Synapse for Neuromorphic Computing

Suman Hu, Jaehyun Kang, Taeyoon Kim, Suyoun Lee, Jong Keuk Park, Inho Kim, Jaewook Kim, Joon Young Kwak, Jongkil Park, Gyu Tae Kim, Shinhyun Choi, Yeonjoo Jeong

Research output: Contribution to journalArticlepeer-review

Abstract

Neuromorphic hardware is a system with massive potential to enable efficient computing by mimicking the human brain. The novel system processes information using neuron spikes (Action Potentials) and the synaptic connections between neurons are trained using biologically plausible methods like spike-timing-dependent plasticity (STDP). Memristor is one of the promising candidates to implement such neuromorphic hardware. Two types of memristors, diffusive and drift, have been proposed to form a synapse showing faithful emulation of STDP, where the diffusion effect is used to trace the spike timing history crucial for STDP and the drift memristor keeps the weight information in a longer time scale. The purpose of this paper is to systematically investigate STDP characteristics in such a synapse with serially connected two memristors using SPICE models. The results show that STDP properties are strongly dependent on device parameters and even the shape of STDP curves is modified. Different shapes of the STDP curve were identified. The results and analysis could support the design of emerging device-based synapses, which can faithfully mimic biological STDP characteristics for future neuromorphic systems.

Original languageEnglish
Pages (from-to)6381-6392
Number of pages12
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Keywords

  • Ca2+
  • SPICE simulation
  • STDP
  • diffusive memristor
  • drift memristor
  • hump
  • voltage division

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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