Artificial optic-neural synapse for colored and color-mixed pattern recognition. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Seo, Seunghwan; Jo, Seo-Hyeon; Kim, Sungho; Shim, Jaewoo; Oh, Seyong; Kim, Jeong-Hoon; Heo, Keun; Choi, Jae-Woong; Choi, Changhwan; Oh, Saeroonter; Kuzum, Duygu; Wong, H.-S.; Park, Jin-Hong
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 8
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-07572-5
• Languages: English
• ISSNs: 2041-1723

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 onh -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. Artificial neural networks can emulate the human vision because of their spike-based operation by employing memristors as synapses. Here, Seo et al. integrate synaptic and optical sensing functions in a single heterostructure, which enables accurate and energy-efficient recognition of colored patterns.