Organic Memristor-Based Flexible Neural Networks with Bio-Realistic Synaptic Plasticity for Complex Combinatorial Optimization

Hyeongwook Kim, Miseong Kim, Aejin Lee, Hea Lim Park, Jaewon Jang, Jin Hyuk Bae, In Man Kang, Eun Sol Kim, Sin Hyung Lee

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Several studies have been conducted on flexible neural networks for practical applications; however, developing systems with complete synaptic plasticity for combinatorial optimization remains challenging. In this study, the metal-ion injection density is explored as a diffusive parameter of the conductive filament in organic memristors. Additionally, a flexible artificial synapse with bio-realistic synaptic plasticity is developed using organic memristors that have systematically engineered metal-ion injections, for the first time. In the proposed artificial synapse, short-term plasticity (STP), long-term plasticity, and homeostatic plasticity are independently achieved and are analogous to their biological counterparts. The time windows of the STP and homeostatic plasticity are controlled by the ion-injection density and electric-signal conditions, respectively. Moreover, stable capabilities for complex combinatorial optimization in the developed synapse arrays are demonstrated under spike-dependent operations. This effective concept for realizing flexible neuromorphic systems for complex combinatorial optimization is an essential building block for achieving a new paradigm of wearable smart electronics associated with artificial intelligent systems.

Original languageEnglish
Article number2300659
JournalAdvanced Science
Volume10
Issue number19
DOIs
StatePublished - 6 Jul 2023

Keywords

  • artificial synapse
  • combinatorial optimization
  • flexible neural network
  • organic memristor
  • synaptic plasticity

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