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An ultrasmall organic synapse for neuromorphic computing

Authors :
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
Source :
Nature Communications, Vol 14, Iss 1, Pp 1-10 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.070df895889f48bdb0469ba39c854ef8
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-023-43542-2