GaN-based optoelectronic synapses

Jianya Zhang; Jiamin Li; Mingmin Zhong; Qiyu Xu; Yibin Wang; Haoran Li; Yuxin Yang; Yukun Zhao

doi:10.1088/1674-4926/26020020

J. Semicond. > Just Accepted

RESEARCH HIGHLIGHTS

GaN-based optoelectronic synapses

Jianya Zhang^{1, §}, Jiamin Li^{1, §}, Mingmin Zhong^{1, §}, Qiyu Xu^{2, 3, §}, Yibin Wang¹, Haoran Li¹, Yuxin Yang¹ and Yukun Zhao^{2, 3, 4,}

+ Author Affiliations

1. Key Laboratory of Intelligent Optoelectronic Devices and Chips of Jiangsu Higher Education Institutions, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou 215009, China
2. School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, Hefei 230026, China
3. Key Laboratory of Semiconductor Display Materials and Chips, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences (CAS), Suzhou 215123, China
4. Guangdong Institute of Semiconductor Micro-Nano Manufacturing Technology, Foshan 528225, China
^§Jianya Zhang, Jiamin Li, Mingmin Zhong and Qiyu Xu contributed equally to this work and should be considered as co-first authors.

Author Bio:

Jianya Zhang works in Suzhou University of Science and Technology. She mainly studies the GaN-based semiconductor. She received the PhD degree in 2022 at University of Science and Technology of China. As the first leader, she has guided a NSFC project, a Special Support Project of China Postdoctoral Science Foundation, and a General Program. She has been awarded several academic awards, such as Double-Innovation Doctor and Vice President of Technology Company in Jiangsu Province, etc. Furthermore, he has been authorized the USA and Japanese patents.

Yukun Zhao is a Research Fellow at Chinese Academy of Sciences and a Postgraduate Supervisor at University of Science and Technology of China. He received the double BS degrees in 2012 and PhD degree in 2017 at Xi'an Jiaotong University. Supported by national official scholarships, he studied in University of Liverpool (UK) and Leibniz Association (Germany) for 2 years. Nowadays, he serves as the Youth Editorial Board Member for journals Innovation, Exploration and Chip, etc. He is also a reviewer for several national and provincial projects. His research interests include GaN-based nanowires, semiconductor chips, etc.

Corresponding author: Yukun Zhao, ykzhao2017@sinano.ac.cn

DOI: 10.1088/1674-4926/26020020CSTR: 32376.14.1674-4926.26020020

References

[1]	Pazos S, Zhu K C, Villena M A, et al. Synaptic and neural behaviours in a standard silicon transistor. Nature, 2025, 640(8057): 69 doi: 10.1038/s41586-025-08742-4
[2]	Yang D P, Chen H, Tang Z H, et al. Optoelectronic synapses based on IGZO/Bi_3.25La_0.75Ti₃O₁₂ heterojunctions for human brain learning mechanism simulation. J Semicond, 2026, 47(1): 012403 doi: 10.1088/1674-4926/25060032
[3]	Chen K, Hu H, Song I, et al. Organic optoelectronic synapse based on photon-modulated electrochemical doping. Nat Photon, 2023, 17(7): 629 doi: 10.1038/s41566-023-01232-x
[4]	Kwon J Y, Kim J E, Kim J S, et al. Artificial sensory system based on memristive devices. Exploration, 2024, 4(1): 20220162 doi: 10.1002/EXP.20220162
[5]	Khan A I, Kim J K, Sikder U, et al. Negative capacitance overcomes Schottky-gate limits in GaN high-electron-mobility transistors. Science, 2025, 389(6759): 508 doi: 10.1126/science.adx6955
[6]	Kumar A S, Dalcanale S, Uren M J, et al. Gallium nitride multichannel devices with latch-induced sub-60-mV-per-decade subthreshold slopes for radiofrequency applications. Nat Electron, 2025, 8(6): 510 doi: 10.1038/s41928-025-01391-5
[7]	Jiang M, Zhao Y K, Liu T, et al. A dual-mode transparent device for 360° quasi-omnidirectional self-driven photodetection and efficient ultralow-power neuromorphic computing. Light Sci Appl, 2025, 14(1): 273 doi: 10.1038/s41377-025-01991-y
[8]	Hong X T, Huang Y L, Tian Q L, et al. Two-dimensional perovskite-gated AlGaN/GaN high-electron-mobility-transistor for neuromorphic vision sensor. Adv Sci, 2022, 9(27): 2202019
[9]	Lu Y T, Rao Z L, Shim H, et al. A neuromorphic imager based on a cascaded optoelectronic synapse. Nat Electron, 2026, 9(2): 180 doi: 10.1038/s41928-025-01540-w
[10]	Sun Z H, Shi F, Shi Z, et al. GaN optoelectronic integrated chip with multifunctions of communication and neuromorphic computing. Adv Opt Mater, 2025, 13(18): 70007 doi: 10.1002/adom.70007
[11]	Zhang J Y, Li J M, Yang L B, et al. Electric-stimulated controllable synaptic GaN nano-device for neuromorphic computing. Chip, 2025, 4(4): 100149 doi: 10.1016/j.chip.2025.100149
[12]	Hua X Y, Zheng J Y, Han X, et al. Artificial optoelectronic synapse with nanolayered GaN/AlN periodic structure for neuromorphic computing. ACS Appl Nano Mater, 2023, 6(10): 8461 doi: 10.1021/acsanm.3c00796
[13]	Wright W J, Hedrick N G, Komiyama T. Distinct synaptic plasticity rules operate across dendritic compartments in vivo during learning. Science, 2025, 388(6744): 322 doi: 10.1126/science.ads4706
[14]	Gao F L, Li L H, Li S T. Research progress in gallium nitride-based artificial synaptic devices. eScience, 2026, 6(3): 100519 doi: 10.1016/j.esci.2025.100519
[15]	Li Y, Shen G Z. Advances in optoelectronic artificial synapses. Cell Rep Phys Sci, 2022, 3(9): 101037 doi: 10.1016/j.xcrp.2022.101037
[16]	Cheng L R, Yin J, Hou B B, et al. Multifunctional integrated micro-light-emitting diodes for self-powered photodetection and neuromorphic computing. Cell Rep Phys Sci, 2025, 6(11): 102913 doi: 10.1016/j.xcrp.2025.102913
[17]	Tiw P J, Yuan R, Zhang T, et al. An end-to-end memristive hardware system based on single-spike coding for human–machine interfaces. Nat Electron, 2026, 9(2): 225 doi: 10.1038/s41928-025-01544-6
[18]	Gao Z X, Ju X, Yu H B, et al. Ultrathin gallium nitride quantum-disk-in-nanowire-enabled reconfigurable bioinspired sensor for high-accuracy human action recognition. Nano Micro Lett, 2025, 18(1): 54 doi: 10.1007/s40820-025-01888-w
[19]	Huang Y Q, Wu Q Q, Gong T C, et al. Bayesian neural network with unified entropy source and synapse weights using 3D 16-layer Fe-diode array. Nat Commun, 2025, 16(1): 8063 doi: 10.1038/s41467-025-63302-8
[20]	Harabi K E, Hirtzlin T, Turck C, et al. A memristor-based Bayesian machine. Nat Electron, 2023, 6(1): 52

Fig. 1. (Color online) Preparation of different GaN-based optoelectronic devices. (a) Schematic diagram of the ability of the neuro visual system to recognize images. Reproduced with permission^[8]. Copyright 2022. Advanced Science. (b) Schematic diagram and the layered structure of the GaN optoelectronic integrated chip with multi-functions. Reproduced with permission^[10]. (c) Cross-sectional scanning transmission electron micro-scope (STEM) image of the top epilayers of the device mentioned in (b). Reproduced with permission^[10]. Copyright 2025. Advanced Optical Materials. (d) Schematic illustrations of the fabricated processes for the dual-mode monolithic GaN-based device^[7]. (e) Scanning electron microscopy (SEM) images showing the side view of (Al,Ga)N/GaN nanowires in (d). Reproduced with permission^[7]. Copyright 2025. Light: Science & Applications. (f) Schematic diagram of the manufacturing process of a single GaN nanowire device^[11]. (g) A top-view SEM image of the synaptic device based on GaN. Reproduced with permission^[11]. Copyright 2025. Chip.

DownLoad: Full-Size Img PowerPoint

Fig. 2. (Color online) The synaptic function and application of GaN optoelectronic synaptic devices. (a) Humanoid robot equipped with GaN-based synaptic devices. Reproduced with permission^[16]. Copyright 2025. Cell Reports Physical Science. (b) The image perception, preprocessing and memory computing of device under different bias voltages. Reproduced with permission^[18]. Copyright 2025. Nano-Micro Letters. (c) The image preprocessing using three different hardware kernels. Reproduced with permission^[8]. Copyright 2022. Advanced Science. (d) Optical micrograph of the Bayesian system. Details of the likelihood block, which includes digital circuitry and a memory block. SEM of a memristor in the back-end of the hybrid memristors. Reproduced with permission^[20]. Copyright 2023. Nature Electronics.

DownLoad: Full-Size Img PowerPoint

[1]	Pazos S, Zhu K C, Villena M A, et al. Synaptic and neural behaviours in a standard silicon transistor. Nature, 2025, 640(8057): 69 doi: 10.1038/s41586-025-08742-4
[2]	Yang D P, Chen H, Tang Z H, et al. Optoelectronic synapses based on IGZO/Bi_3.25La_0.75Ti₃O₁₂ heterojunctions for human brain learning mechanism simulation. J Semicond, 2026, 47(1): 012403 doi: 10.1088/1674-4926/25060032
[3]	Chen K, Hu H, Song I, et al. Organic optoelectronic synapse based on photon-modulated electrochemical doping. Nat Photon, 2023, 17(7): 629 doi: 10.1038/s41566-023-01232-x
[4]	Kwon J Y, Kim J E, Kim J S, et al. Artificial sensory system based on memristive devices. Exploration, 2024, 4(1): 20220162 doi: 10.1002/EXP.20220162
[5]	Khan A I, Kim J K, Sikder U, et al. Negative capacitance overcomes Schottky-gate limits in GaN high-electron-mobility transistors. Science, 2025, 389(6759): 508 doi: 10.1126/science.adx6955
[6]	Kumar A S, Dalcanale S, Uren M J, et al. Gallium nitride multichannel devices with latch-induced sub-60-mV-per-decade subthreshold slopes for radiofrequency applications. Nat Electron, 2025, 8(6): 510 doi: 10.1038/s41928-025-01391-5
[7]	Jiang M, Zhao Y K, Liu T, et al. A dual-mode transparent device for 360° quasi-omnidirectional self-driven photodetection and efficient ultralow-power neuromorphic computing. Light Sci Appl, 2025, 14(1): 273 doi: 10.1038/s41377-025-01991-y
[8]	Hong X T, Huang Y L, Tian Q L, et al. Two-dimensional perovskite-gated AlGaN/GaN high-electron-mobility-transistor for neuromorphic vision sensor. Adv Sci, 2022, 9(27): 2202019
[9]	Lu Y T, Rao Z L, Shim H, et al. A neuromorphic imager based on a cascaded optoelectronic synapse. Nat Electron, 2026, 9(2): 180 doi: 10.1038/s41928-025-01540-w
[10]	Sun Z H, Shi F, Shi Z, et al. GaN optoelectronic integrated chip with multifunctions of communication and neuromorphic computing. Adv Opt Mater, 2025, 13(18): 70007 doi: 10.1002/adom.70007
[11]	Zhang J Y, Li J M, Yang L B, et al. Electric-stimulated controllable synaptic GaN nano-device for neuromorphic computing. Chip, 2025, 4(4): 100149 doi: 10.1016/j.chip.2025.100149
[12]	Hua X Y, Zheng J Y, Han X, et al. Artificial optoelectronic synapse with nanolayered GaN/AlN periodic structure for neuromorphic computing. ACS Appl Nano Mater, 2023, 6(10): 8461 doi: 10.1021/acsanm.3c00796
[13]	Wright W J, Hedrick N G, Komiyama T. Distinct synaptic plasticity rules operate across dendritic compartments in vivo during learning. Science, 2025, 388(6744): 322 doi: 10.1126/science.ads4706
[14]	Gao F L, Li L H, Li S T. Research progress in gallium nitride-based artificial synaptic devices. eScience, 2026, 6(3): 100519 doi: 10.1016/j.esci.2025.100519
[15]	Li Y, Shen G Z. Advances in optoelectronic artificial synapses. Cell Rep Phys Sci, 2022, 3(9): 101037 doi: 10.1016/j.xcrp.2022.101037
[16]	Cheng L R, Yin J, Hou B B, et al. Multifunctional integrated micro-light-emitting diodes for self-powered photodetection and neuromorphic computing. Cell Rep Phys Sci, 2025, 6(11): 102913 doi: 10.1016/j.xcrp.2025.102913
[17]	Tiw P J, Yuan R, Zhang T, et al. An end-to-end memristive hardware system based on single-spike coding for human–machine interfaces. Nat Electron, 2026, 9(2): 225 doi: 10.1038/s41928-025-01544-6
[18]	Gao Z X, Ju X, Yu H B, et al. Ultrathin gallium nitride quantum-disk-in-nanowire-enabled reconfigurable bioinspired sensor for high-accuracy human action recognition. Nano Micro Lett, 2025, 18(1): 54 doi: 10.1007/s40820-025-01888-w
[19]	Huang Y Q, Wu Q Q, Gong T C, et al. Bayesian neural network with unified entropy source and synapse weights using 3D 16-layer Fe-diode array. Nat Commun, 2025, 16(1): 8063 doi: 10.1038/s41467-025-63302-8
[20]	Harabi K E, Hirtzlin T, Turck C, et al. A memristor-based Bayesian machine. Nat Electron, 2023, 6(1): 52

Search

GET CITATION

shu

Export: BibTex EndNote

Article Metrics

Article views: 11 Times PDF downloads: 0 Times Cited by: 0 Times

History

Received: 06 February 2026 Revised: 17 March 2026 Online: Accepted Manuscript: 14 April 2026

Article Navigation > Journal of Semiconductors > 2026 > Accepted Manuscript

Jianya Zhang, Jiamin Li, Mingmin Zhong, Qiyu Xu, Yibin Wang, Haoran Li, Yuxin Yang, Yukun Zhao. GaN-based optoelectronic synapses[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/26020020 ****J Y Zhang, J M Li, M M Zhong, Q Y Xu, Y B Wang, H R Li, Y X Yang, and Y K Zhao, GaN-based optoelectronic synapses[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26020020

Citation:

Jianya Zhang, Jiamin Li, Mingmin Zhong, Qiyu Xu, Yibin Wang, Haoran Li, Yuxin Yang, Yukun Zhao. GaN-based optoelectronic synapses[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/26020020 ****

J Y Zhang, J M Li, M M Zhong, Q Y Xu, Y B Wang, H R Li, Y X Yang, and Y K Zhao, GaN-based optoelectronic synapses[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26020020

Citation:

J Y Zhang, J M Li, M M Zhong, Q Y Xu, Y B Wang, H R Li, Y X Yang, and Y K Zhao, GaN-based optoelectronic synapses[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26020020

PDF( 3933 KB)

GaN-based optoelectronic synapses

DOI: 10.1088/1674-4926/26020020

CSTR: 32376.14.1674-4926.26020020

Jianya Zhang^{1, §},
Jiamin Li^{1, §},
Mingmin Zhong^{1, §},
Qiyu Xu^{2, 3, §},
Yibin Wang¹,
Haoran Li¹,
Yuxin Yang¹,
Yukun Zhao^{2, 3, 4,}

1.
Key Laboratory of Intelligent Optoelectronic Devices and Chips of Jiangsu Higher Education Institutions, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou 215009, China
2.
School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, Hefei 230026, China
3.
Key Laboratory of Semiconductor Display Materials and Chips, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences (CAS), Suzhou 215123, China
4.
Guangdong Institute of Semiconductor Micro-Nano Manufacturing Technology, Foshan 528225, China

More Information

Jianya Zhang works in Suzhou University of Science and Technology. She mainly studies the GaN-based semiconductor. She received the PhD degree in 2022 at University of Science and Technology of China. As the first leader, she has guided a NSFC project, a Special Support Project of China Postdoctoral Science Foundation, and a General Program. She has been awarded several academic awards, such as Double-Innovation Doctor and Vice President of Technology Company in Jiangsu Province, etc. Furthermore, he has been authorized the USA and Japanese patents
Yukun Zhao is a Research Fellow at Chinese Academy of Sciences and a Postgraduate Supervisor at University of Science and Technology of China. He received the double BS degrees in 2012 and PhD degree in 2017 at Xi'an Jiaotong University. Supported by national official scholarships, he studied in University of Liverpool (UK) and Leibniz Association (Germany) for 2 years. Nowadays, he serves as the Youth Editorial Board Member for journals Innovation, Exploration and Chip, etc. He is also a reviewer for several national and provincial projects. His research interests include GaN-based nanowires, semiconductor chips, etc
Corresponding author: ykzhao2017@sinano.ac.cn
Received Date: 2026-02-06
Revised Date: 2026-03-17

Available Online: 2026-04-14

Abstract

^§Jianya Zhang, Jiamin Li, Mingmin Zhong and Qiyu Xu contributed equally to this work and should be considered as co-first authors.

FullText(HTML)

References(20)