Organic electronic synapses with low energy consumption

Yeongjun Lee, Hea Lim Park, Yeongin Kim, Tae Woo Lee

Research output: Contribution to journalReview articlepeer-review

130 Scopus citations

Abstract

The von Neumann computing architecture consists of separated processing and memory elements; it is too bulky and energy-intensive to be implemented in the upcoming artificial intelligence age. In contrast, neurons and synapses in a brain perform learning and memory in an integrated manner and function energy-efficiently by analog adjustment of synaptic strengths in response to stimulation. Organic artificial synapses provide good emulation of the functions and structures of biological synapses and are easily fabricated and therefore can be applied to various neuromorphic electronic devices. In particular, organic artificial synapses that consume energy at a level comparable to that of a biological synapse show great promise for use in future low-energy neuromorphic devices. Here, we review the trends of energy consumption of organic artificial synapses and how it is affected by the structure, materials, and operation mechanism. We also present a strategy to decrease the energy consumption of organic neuromorphic devices. Our review will help the development of versatile low-energy organic neuromorphic electronics.

Original languageEnglish
Pages (from-to)794-810
Number of pages17
JournalJoule
Volume5
Issue number4
DOIs
StatePublished - 21 Apr 2021

Keywords

  • artificial intelligence
  • energy efficiency
  • neuromorphic electronics
  • organic neuroprosthetics
  • power efficiency

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