Artificial optic-neural synapse for colored and color-mixed pattern recognition

Seunghwan Seo, Seo-Hyeon Jo, Sungho Kim, Jaewoo Shim, Seyong Oh, Jeong-Hoon Kim, Keun Heo, Jae-Woong Choi, Changhwan Choi, Saeroonter Oh, Duygu Kuzum, H.-S. Philip Wong and Jin-Hong Park ()
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Seunghwan Seo: Sungkyunkwan University
Seo-Hyeon Jo: Sungkyunkwan University
Sungho Kim: Sungkyunkwan University
Jaewoo Shim: Sungkyunkwan University
Seyong Oh: Sungkyunkwan University
Jeong-Hoon Kim: Sungkyunkwan University
Keun Heo: Sungkyunkwan University
Jae-Woong Choi: Sungkyunkwan University
Changhwan Choi: Hanyang University
Saeroonter Oh: Hanyang University
Duygu Kuzum: University of California, San Diego
H.-S. Philip Wong: Stanford University
Jin-Hong Park: Sungkyunkwan University

Nature Communications, 2018, vol. 9, issue 1, 1-8

Abstract: Abstract The priority of synaptic device researches has been given to prove the device potential for the emulation of synaptic dynamics and not to functionalize further synaptic devices for more complex learning. Here, we demonstrate an optic-neural synaptic device by implementing synaptic and optical-sensing functions together on h-BN/WSe2 heterostructure. This device mimics the colored and color-mixed pattern recognition capabilities of the human vision system when arranged in an optic-neural network. Our synaptic device demonstrates a close to linear weight update trajectory while providing a large number of stable conduction states with less than 1% variation per state. The device operates with low voltage spikes of 0.3 V and consumes only 66 fJ per spike. This consequently facilitates the demonstration of accurate and energy efficient colored and color-mixed pattern recognition. The work will be an important step toward neural networks that comprise neural sensing and training functions for more complex pattern recognition.

Date: 2018
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DOI: 10.1038/s41467-018-07572-5

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