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Cited 12 time in webofscience Cited 12 time in scopus
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A Neuromorphic Device Implemented on a Salmon-DNA Electrolyte and its Application to Artificial Neural Networksopen access

Authors
Kong, Dong-HoKim, Jeong-HoonOh, SeyongPark, Hyung-YoulDugasani, Sreekantha ReddyKang, Beom-SeokChoi, ChanghwonChoi, RinoLee, SungjooPark, Sung HaHeo, KeunPark, Jin-Hong
Issue Date
Sep-2019
Publisher
WILEY
Keywords
handwritten digit pattern recognition; neural devices; neuromorphic devices; salmon DNA; synaptic devices
Citation
ADVANCED SCIENCE, v.6, no.17, pp.1 - 8
Indexed
SCIE
SCOPUS
Journal Title
ADVANCED SCIENCE
Volume
6
Number
17
Start Page
1
End Page
8
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/12547
DOI
10.1002/advs.201901265
ISSN
2198-3844
Abstract
A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+-doped salmon deoxyribonucleic acid (S-DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S-DNA electrolyte, the synaptic operation of the S-DNA device features special long-term plasticity with negative and positive nonlinearity values for potentiation and depression (alpha(p) and alpha(d)), respectively, which consequently improves the learning recognition efficiency of S-DNA-based neural networks. Furthermore, the representative neuronal operation, "integrateand-fire," is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S-DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (vertical bar alpha(p)vertical bar: 31 -> 20, vertical bar alpha(d)vertical bar: 11 -> 18 weight update margin: 33 -> 287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single-layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S-DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy.
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