Biodegradable and Flexible Polymer-Based Memristor Possessing Optimized Synaptic Plasticity for Eco-Friendly Wearable Neural Networks with High Energy Efficiency

Sungjun Oh, Hyungjin Kim, Seong Eun Kim, Min Hwi Kim, Hea Lim Park, Sin Hyung Lee

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Organic memristors are promising candidates for the flexible synaptic components of wearable intelligent systems. With heightened concerns for the environment, considerable effort has been made to develop organic transient memristors to realize eco-friendly flexible neural networks. However, in the transient neural networks, achieving flexible memristors with biorealistic synaptic plasticity for energy efficient learning processes is still challenging. Herein, a biodegradable and flexible polymer-based memristor, suitable for the spike-dependent learning process, is demonstrated. An electrochemical metallization phenomenon for the conductive nanofilament growth in a polymer medium of poly (vinyl alcohol) (PVA) is analyzed and a PVA-based transient and flexible artificial synapse is developed. The developed device exhibits superior biodegradability and stable mechanical flexibility due to the high water solubility and excellent tensile strength of the PVA film, respectively. In addition, the developed flexible memristor is operated as a reliable synaptic device with optimized synaptic plasticity, which is ideal for artificial neural networks with the spike-dependent operations. The developed device is found to be effectively served as a reliable synaptic component with high energy efficiency in practical neural networks. This novel strategy for developing transient and flexible artificial synapses can be a fundamental platform for realizing eco-friendly wearable intelligent systems. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166603245.58711630/v1.

Original languageEnglish
Article number2200272
JournalAdvanced Intelligent Systems
Volume5
Issue number5
DOIs
StatePublished - May 2023

Keywords

  • artificial synapses
  • flexible memristors
  • neural networks
  • synaptic function
  • transient memristors

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