Nature Communications (Nov 2023)

An ultrasmall organic synapse for neuromorphic computing

  • Shuzhi Liu,
  • Jianmin Zeng,
  • Zhixin Wu,
  • Han Hu,
  • Ao Xu,
  • Xiaohe Huang,
  • Weilin Chen,
  • Qilai Chen,
  • Zhe Yu,
  • Yinyu Zhao,
  • Rong Wang,
  • Tingting Han,
  • Chao Li,
  • Pingqi Gao,
  • Hyunwoo Kim,
  • Seung Jae Baik,
  • Ruoyu Zhang,
  • Zhang Zhang,
  • Peng Zhou,
  • Gang Liu

DOI
https://doi.org/10.1038/s41467-023-43542-2
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 10

Abstract

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Abstract High‐performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain‐inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high‐density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL10 with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as‐fabricated PBFCL10 synapses can switch between 32 conductance states linearly with a high cycle‐to‐cycle uniformity of 98.89% and device‐to‐device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking‐plasticity‐related algorithm for decision-making tasks.