A Neuromorphic Device Implemented on a Salmon-DNA Electrolyte and its Application to Artificial Neural Networks

被引:39
作者
Kong, Dong-Ho [1 ,2 ]
Kim, Jeong-Hoon [1 ]
Oh, Seyong [1 ]
Park, Hyung-Youl [1 ]
Dugasani, Sreekantha Reddy [3 ]
Kang, Beom-Seok [1 ]
Choi, Changhwon [4 ]
Choi, Rino [5 ]
Lee, Sungjoo [6 ]
Park, Sung Ha [3 ]
Heo, Keun [1 ]
Park, Jin-Hong [1 ,6 ]
机构
[1] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon 16419, South Korea
[2] Nanyang Technol Univ, Sch Elect & Elect Engn, 50 Nanyang Ave, Singapore 639798, Singapore
[3] Sungkyunkwan Univ, Dept Phys, Suwon 440746, South Korea
[4] Hanyang Univ, Div Mat Sci & Engn, Seoul 133791, South Korea
[5] Inha Univ, Mat Sci & Engn, Incheon 402751, South Korea
[6] Sungkyunkwan Univ, SKKU Adv Inst Nanotechnol SAINT, Suwon 440746, South Korea
基金
新加坡国家研究基金会;
关键词
handwritten digit pattern recognition; neural devices; neuromorphic devices; salmon DNA; synaptic devices; SYNAPTIC DEVICE; PLASTICITY; DESIGN;
D O I
10.1002/advs.201901265
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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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页数:8
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