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Issue 6, 2019
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Fully transparent, flexible and waterproof synapses with pattern recognition in organic environments

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Abstract

Artificial intelligence applications require bio-inspired neuromorphic systems that consist of electronic synapses (e-synapses) able to perform learning and memory functions. However, all transparent and flexible organic e-synapses have the disadvantage of being easily dissolvable in water or organic solutions. In the present work, a stable waterproof artificial synapse based on a fully transparent electronic device, suitable for wearable applications in organic environments is for the first time demonstrated. Essential synaptic behaviors, including paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/LTD), and learning–forgetting–relearning, were successfully emulated. The artificial synaptic device could achieve an optical transmittance of ∼87.5% in the visible light range, which demonstrated reliable long-term potentiation/depression under bent states with a bending radius of 5 mm. After being immersed in water and 5 types of common organic solvents for over 12 hours, the e-synapse could function with 6000 spikes without noticeable degradation in the organic environment. The neural network was constructed from e-synapses with controllable weights update and a device-to-system level simulation framework was developed with a recognition rate of 92.4%, which demonstrated the feasibility of highly transparent, biocompatible, flexible, and waterproof e-synapses used in artificial intelligence systems.

Graphical abstract: Fully transparent, flexible and waterproof synapses with pattern recognition in organic environments

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Supplementary files

Article information


Submitted
23 May 2019
Accepted
10 Jun 2019
First published
11 Jun 2019

Nanoscale Horiz., 2019,4, 1293-1301
Article type
Communication

Fully transparent, flexible and waterproof synapses with pattern recognition in organic environments

T. Wang, J. Meng, Z. He, L. Chen, H. Zhu, Q. Sun, S. Ding, P. Zhou and D. W. Zhang, Nanoscale Horiz., 2019, 4, 1293
DOI: 10.1039/C9NH00341J

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