Cargando…
Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications
Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic me...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10005145/ https://www.ncbi.nlm.nih.gov/pubmed/36903681 http://dx.doi.org/10.3390/nano13050803 |
_version_ | 1784905007950200832 |
---|---|
author | Zeng, Jianmin Chen, Xinhui Liu, Shuzhi Chen, Qilai Liu, Gang |
author_facet | Zeng, Jianmin Chen, Xinhui Liu, Shuzhi Chen, Qilai Liu, Gang |
author_sort | Zeng, Jianmin |
collection | PubMed |
description | Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO(4))](2)/triphenylamine-containing polymer (BTPA-F) redox system. The device with bilayer structure organic materials as the resistive switching layer (RSL) exhibits memristive behaviors and excellent long-term synaptic plasticity. Additionally, the device’s conductance states can be precisely modulated by consecutively applying voltage pulses between the top and bottom electrodes. A three-layer perception neural network with in situ computing enabled was then constructed utilizing the proposed memristor and trained on the basis of the device’s synaptic plasticity characteristics and conductance modulation rules. Recognition accuracies of 97.3% and 90% were achieved, respectively, for the raw and 20% noisy handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, demonstrating the feasibility and applicability of implementing neuromorphic computing applications utilizing the proposed organic memristor. |
format | Online Article Text |
id | pubmed-10005145 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100051452023-03-11 Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications Zeng, Jianmin Chen, Xinhui Liu, Shuzhi Chen, Qilai Liu, Gang Nanomaterials (Basel) Article Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO(4))](2)/triphenylamine-containing polymer (BTPA-F) redox system. The device with bilayer structure organic materials as the resistive switching layer (RSL) exhibits memristive behaviors and excellent long-term synaptic plasticity. Additionally, the device’s conductance states can be precisely modulated by consecutively applying voltage pulses between the top and bottom electrodes. A three-layer perception neural network with in situ computing enabled was then constructed utilizing the proposed memristor and trained on the basis of the device’s synaptic plasticity characteristics and conductance modulation rules. Recognition accuracies of 97.3% and 90% were achieved, respectively, for the raw and 20% noisy handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, demonstrating the feasibility and applicability of implementing neuromorphic computing applications utilizing the proposed organic memristor. MDPI 2023-02-22 /pmc/articles/PMC10005145/ /pubmed/36903681 http://dx.doi.org/10.3390/nano13050803 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zeng, Jianmin Chen, Xinhui Liu, Shuzhi Chen, Qilai Liu, Gang Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications |
title | Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications |
title_full | Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications |
title_fullStr | Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications |
title_full_unstemmed | Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications |
title_short | Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications |
title_sort | organic memristor with synaptic plasticity for neuromorphic computing applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10005145/ https://www.ncbi.nlm.nih.gov/pubmed/36903681 http://dx.doi.org/10.3390/nano13050803 |
work_keys_str_mv | AT zengjianmin organicmemristorwithsynapticplasticityforneuromorphiccomputingapplications AT chenxinhui organicmemristorwithsynapticplasticityforneuromorphiccomputingapplications AT liushuzhi organicmemristorwithsynapticplasticityforneuromorphiccomputingapplications AT chenqilai organicmemristorwithsynapticplasticityforneuromorphiccomputingapplications AT liugang organicmemristorwithsynapticplasticityforneuromorphiccomputingapplications |