Analysis of the Operating Mechanism of Optoelectronic Synaptic Devices: From Materials to Device

Authors

  • Qiunan Li Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, China

DOI:

https://doi.org/10.62051/cxqjaj64

Keywords:

Optoelectronic synaptic; Materials; Mechanism.

Abstract

Optoelectronic synaptic devices have garnered significant attention for their potential in neuromorphic computing, offering a pathway to overcome the limitations of von Neumann architectures by enabling parallel processing, low power consumption, and integrated sensing-memory-processing functionalities. These devices emulate biological synapses through mechanisms such as light-induced charge trapping/detrapping and light-driven ion migration, which support synaptic plasticity and non-volatile memory. However, performance limitations persist due to dependencies on device architecture and material properties. This article provides a comprehensive analysis of how different structural configurations—including two-terminal and three-terminal designs—along with various material systems, such as silicon-based semiconductors, two-dimensional materials, perovskites, organic polymers, and MXene-based composites, critically influence operational characteristics like responsivity, switching speed, energy efficiency, and environmental stability. By reviewing recent advances in heterojunction engineering and ion-gated transistors, this study underscores the importance of optimizing both material selection and device geometry to achieve high-performance, biomimetic optoelectronic synapses suitable for next-generation visual and cognitive computing applications.

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References

[1] Zhao Y, Cheng L, Zhang J, et al. Research progress and applications of optoelectronic synaptic devices based on 2D materials. Brain-X, 2024, 2(3): e70004.

[2] Su F, Wang Z, Wan J. et al. Recent advances in optoelectronic synapses: from advanced materials to neuromorphic applications. Rare Met, 2025, 07: 1-32.

[3] Liang K, Ren H, Zhu B, et al. Tunable Plasticity in Printed Optoelectronic Synaptic Transistors by Contact Engineering. IEEE Electron Device Letters, 2022, 43(6): 882–885.

[4] Serway, R.A., Moses, C.J. and Moyer, C.A. Modern physics. 6th edn. Boston, MA: Cengage Learning. 2022

[5] Yoo H, Lee I, Kim H, et al. A review of phototransistors using metal oxide semiconductors: research progress and future directions. Adv Mater, 2021, 33(47):2006091.

[6] Liu Z, Wang Y, Ye Z, et al. Harnessing defects in SnSe film via photo-induced doping for fully light-controlled artificial synapse. Advanced materials (Weinheim), 2025, 37 (4): e2410783

[7] Xie Pengshan. Research on artificial synaptic devices based on InGaAs nanowires for neural network computing. City University of Hong Kong, 2024.

[8] Han X, Xu Z, Pan C, et al. Recent Progress in Optoelectronic Synapses for Artificial Visual‐Perception System. Small Structures, 2020, 1(3): 2000029.

[9] Jiang J, Guo J, Wan Q, et al. 2D MoS2 Neuromorphic Devices for Brain-Like Computational Systems. Small. 2017, 13 (29): 1700933.

[10] Chen Y, Qiu W, Sun J, et al. Solar-blind SnO2 nanowire photo-synapses for associative learning and coincidence detection. Nano. Energy. 2019, 62: 393-400.

[11] Lee M, Nam S, Son H, et al. Accelerated Learning in Wide-Band-Gap AlN Artificial Photonic Synaptic Devices: Impact on Suppressed Shallow Trap Level. Nano Letters, 2021, 21(18): 7879–7886.

[12] Yu J, Yang X, Wang Z, et al. Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure. Science Advances, 2021, 7(12): 9117.

[13] Chen, J., Zhou, Z., Kim B. J, et al. Optoelectronic graded neurons for bioinspired in-sensor motion perception. Nat. Nanotechnol, 2023, 18: 882–888.

[14] Lin Y, Wang W, Li Y, et al. Multifunctional optoelectronic memristor based on CeO2/MoS2 heterojunction for advanced artificial synapses and bionic visual system with nociceptive sensing. Nano Energy, 2024, 121: 109267.

[15] Kim S. J, Im I. H., Baek J. H. et al. Linearly programmable two-dimensional halide perovskite memristor arrays for neuromorphic computing. Nat. Nanotechnol, 2025, 20: 83–92.

[16] Wu X, Wang S, Huang W, et al. Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning. Nat Commun, 2023, 14(1):468.

[17] Zhang J, Guo P, Huang J, et al. Retina-inspired artificial synapses with ultraviolet to near-infrared broadband responses for energy-efficient neuromorphic visual systems. Adv Funct Mater, 2023, 33(32): 2302885.

[18] Ma H, Fang H, Xie X, et al. Optoelectronic Synapses Based on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual-Olfactory Crossmodal Perception. Nano-Micro Lett, 2024, 16: 104.

[19] Xie P, Xu Y, Ho J. C, et al. Birdlike broadband neuromorphic visual sensor arrays for fusion imaging. Nature Communications, 2024, 15(1): 8298.SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.

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Published

22-01-2026

How to Cite

Li, Q. (2026). Analysis of the Operating Mechanism of Optoelectronic Synaptic Devices: From Materials to Device. Transactions on Environment, Energy and Earth Sciences, 5, 32-37. https://doi.org/10.62051/cxqjaj64