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Biodegradable and Flexible Polymer Based Memristor Possessing Optimized Synaptic Plasticity for Eco-Friendly Wearable Neural Networks with High Energy Efficiency       
  • +3
  • Sungjun Oh,
  • Hyungjin Kim,
  • Seong Eun Kim,
  • Min-Hwi Kim,
  • Hea-Lim Park,
  • Sin-Hyung Lee
Sungjun Oh
Hyungjin Kim
Seong Eun Kim
Min-Hwi Kim
Hea-Lim Park
Sin-Hyung Lee

Corresponding Author:sinhlee@knu.ac.kr

Author Profile

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 bio-realistic synaptic plasticity for energy efficient learning processes is still challenging. Here, we demonstrate a biodegradable and flexible polymer based memristor, suitable for the spike-dependent learning process. 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.
Corresponding author(s) Email:   sinhlee@knu.ac.kr 
16 Oct 2022Submitted to AISY Interactive Papers
17 Oct 2022Published in AISY Interactive Papers