A neuromorphic device implemented on a salmon-DNA electrolyte and its application to artificial neural networks

• Main Authors: Kang, Dong-Ho, Kim, Jeong-Hoon, Oh, Seyong, Park, Hyung-Youl, Dugasani, Sreekantha Reddy, Kang, Beom-Seok, Choi, Changhwan, Choi, Rino, Lee, Sungjoo, Park, Sung Ha, Heo, Keun, Park, Jin-Hong
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Electrical and electronic engineering; Handwritten Digit Pattern Recognition; Neural Devices
• Online Access: https://hdl.handle.net/10356/143405
• Institution: Nanyang Technological University

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 (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S-DNA-based neural networks. Furthermore, the representative neuronal operation, "integrate-and-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 (|αp|: 31→20, |αd|: 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.