4.8 Article

Fully photon modulated heterostructure for neuromorphic computing

Journal

NANO ENERGY
Volume 65, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.nanoen.2019.104000

Keywords

Heterostructure; Neuromorphic device; Artificial synapse; Memristor; Photonic synapse

Funding

  1. Natural Science Foundation of China [61601305, 61604097]
  2. Guangdong Province Special Support Plan for High-Level Talents [2017TQ04X082]
  3. Guangdong Provincial Department of Science and Technology [2018B030306028]
  4. Science and Technology Innovation Commission of Shenzhen [JCYJ20180507182042530, JCYJ20180507182000722, JCYJ20170818143618288]
  5. Shenzhen Peacock Technological Innovation Project [KQJSCX20170727100433270, KQJSCX20170327150812967]
  6. NTUT-SZU Joint Research Program [2019006]
  7. Natural Science Foundation of SZU

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Neuromorphic computing has attracted great attention to mimic the brain functions of perception, learning and memory, which are considered to overcome the von Neumann bottleneck. Here, we developed a novel neuromorphic device based on the ZnO/PbS hybrid heterostructure in order to emulate the bio-synaptic activities in the fully photon modulated mode. Owing to regulation of the conduction state, the excitatory and inhibitory activities have been emulated with the excitation of long-wavelength and short-wavelength photons in the ZnO/PbS neuromorphic device. Excitatory plasticity can be mimicked with the UV light, and IR light induces the inhibitory effect. Furthermore, other synapse functions have also been emulated in these modes, including long-term plasticity, short-term plasticity, paired-pulse facilitation/depression, spike-rate-dependent plasticity, etc. Meanwhile, an artificial neural network has been simulated based on the synaptic plasticity of the excitatory and inhibitory effect in the fully photon modulation, and recognition rates up to 67 +/- 6% can be achieved to distinguish the letter images. Our multifunctional artificial synapse based on the fully photon stimulation can open up a new and efficient way for constructing neuromorphic systems.

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