Welcome to Francis Academic Press

International Journal of Frontiers in Engineering Technology, 2024, 6(1); doi: 10.25236/IJFET.2024.060119.

Optoelectronic synapses based on MZO/AZO heterojunction


Zhaoyuan Fan, Zhenghua Tang

Corresponding Author:
Zhenghua Tang

School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, China


In contrast to purely electronic neuromorphic devices, neuromorphic devices stimulated by light Neuromorphic devices stimulated by optical signals are gaining attention for their realistic sensory simulation. A transparent optoelectronic neuromorphic device based on a Mg-doped ZnO/Al-doped ZnO (MZO/AZO) heterostructure memristor has been fabricated in this study. It responds to both electrical and optical signals and successfully simulates various synaptic behaviours, such as STP, LTP, and PPF. In addition, the photomemory mechanism was identified by analysing the energy band structures of MZO and AZO.


MZO/AZO, Optoelectronic, Artificial synapse

Cite This Paper

Zhaoyuan Fan, Zhenghua Tang. Optoelectronic synapses based on MZO/AZO heterojunction. International Journal of Frontiers in Engineering Technology (2024), Vol. 6, Issue 1: 120-124. https://doi.org/10.25236/IJFET.2024.060119.


[1] Song S; Choi C; Ahn J, et al. Artificial optoelectronic synapse based on spatiotemporal irradiation to source‐sharing circuitry of synaptic phototransistors [J]. InfoMat, 2024, 6(2): e12479

[2] Sebastian A; Pannone A; Subbulakshmi R S, et al. Gaussian synapses for probabilistic neural networks [J]. Nature Communications, 2019, 10: 4199.

[3] Lee Y, Park H, Kim Y, et al. Organic electronic synapses with low energy consumption[J]. Joule, 2021, 5: 794-810.

[4] Islam R, Li H, Chen P-Y, et al. Device and materials requirements for neuromorphic computing[J]. Journal of Physics D: Applied Physics, 2019, 52(11): 113001.

[5] Tuma T, Pantazi A, Le Gallo M, et al. Stochastic phase-change neurons[J]. Nature Nanotechnology, 2016, 11(8): 693-699.

[6] Dutta S, Schafer C, Gomez J, et al. Supervised learning in all Fe FET-based spiking neural network: Opportunities and challenges[J]. Frontiers in Neuroscience, 2020, 14(1): 634.

[7] Lv D, Yang Q, Chen Q, et al. All-metal oxide synaptic transistor with modulatable plasticity[J]. Nanotechnology, 2020, 31(6): 65201.

[8] Kim S J, Lee T H, Yang J, et al. Vertically aligned two-dimensional halide perovskites for reliably operable artificial synapses[J]. Materials Today, 2022, 52: 19-30.

[9] Liu Q, Liu Y, Li J, et al. Fully Printed All-Solid-State Organic Flexible Artificial Synapse for Neuromorphic Computing [J]. ACS Applied Materials & Interfaces, 2019, 11(18): 16749-16757.

[10] Kim M, Lee J. Short-Term Plasticity and Long-Term Potentiation in Artificial Biosynapses with Diffusive Dynamics [J]. ACS Nano, 2018, 12(2): 1680-1687.

[11] Lee K C, Li M, Chang Y H, et al. Inverse paired-pulse facilitation in neuroplasticity based on interface-boosted charge trapping layered electronics[J]. Nano Energy, 2020, 77: 105258.

[12] Hao Y, Huang X, Dong M, et al. A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule[J]. Neural Netwoks, 2020, 121: 387-395.

[13] Shrivastava S, Keong L B, Pratik S, at al. Fully Photon Controlled Synaptic Memristor for Neuro-Inspired Computing [J]. Advanced Electronic Materials, 2023, 9(3): 2201093.

[14] Yu J J, Liang L Y, Hu L X, et al. Optoelectronic neuromorphic thin-film transistors capable of selective attention and with ultra-low power dissipation[J]. Nano Energy, 2019, 62:772-780.