Brain-Inspired Topological Surface Modulation for Advanced Nonvolatility in Organic Artificial Synapses

Daegun Kim, Dongyeong Jeong, Kwanghoon Kim, Donghwa Lee, Junho Sung, Hyoik Jang, Kangto Han, Eunho Lee, Geun Yeol Bae

Research output: Contribution to journalArticlepeer-review

Abstract

Human intelligence has evolved dramatically as topological adaptations have taken precedence over volumetric expansion in brain development. Inspired by the remarkable functional enhancements achieved through cortical gyrification, organic synaptic transistors (OSTs) are demonstrated with active layers engineered via controlled surface topologies. Macroscopic compressive forces induce wrinkling in the active polymer layer, generating localized stress that compresses microscopic crystallites. This compression effectively enhances ion retentivity in the OST, leading to improved long-term plasticity (LTP) and greater linearity in synaptic responses compared to uncompressed crystallites. The OST with an optimized topological structure in its active layer exhibits a fourfold enhancement in LTP, successfully emulating paired-pulse facilitation and five key synaptic functions of the human neural system. As a result, simulations of image recognition based on the convolutional neural network demonstrate high accuracy, underscoring the potential of topological control in hardware for artificial neural network computing.

Original languageEnglish
JournalSmall
DOIs
StateAccepted/In press - 2025

Keywords

  • brain-inspired
  • ion retentivity
  • long-term plasticity
  • organic synaptic transistors
  • wrinkling

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