Optoelectronic synapses based on IGZO/Bi3.25La0.75Ti3O12 heterojunctions for human brain learning mechanism simulation

Dongping Yang; Hao Chen; Zhenhua Tang; Qijun Sun; Xingui Tang

doi:10.1088/1674-4926/25060032

J. Semicond. > Just Accepted

ARTICLES

Optoelectronic synapses based on IGZO/Bi₃_.₂₅La₀_.₇₅Ti₃O₁₂ heterojunctions for human brain learning mechanism simulation

Dongping Yang^§, Hao Chen^§, Zhenhua Tang, Qijun Sun and Xingui Tang

+ Author Affiliations

School of Physics and Optoelectric Engineering & Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangdong University of Technology, Guangzhou 510006, China
^§Dongping Yang and Hao Chen contributed equally to this work and should be considered co-first authors.

Author Bio:

Dongping Yang Dong-Ping Yang received his M.S. degree in Electronic Information from Guangdong University of Technology (GDUT), China, in 2023 under the supervision of Prof. Miao He. He is currently pursuing a Ph.D. in Material Physics and Chemistry at GDUT, co-advised by Prof. Xin-Gui Tang and Prof. Qi-Jun Sun. His research focuses on the development of photoelectric synaptic devices for artificial neural network applications. He specializes in thin-film fabrication and characterization, optoelectronic pulse modulation techniques, and neural networkbased pattern recognition training. Dr. Yang has authored 3 first-author SCI papers (6 publications in total) on neuromorphic devices and artificial intelligence hardware systems. His current research interests include neuromorphic computing, brain-inspired intelligent systems, and the integration of optoelectronic materials with machine learning algorithms.

Xingui Tang Xin-Gui Tang, doctoral supervisor of Guangdong University of Technology, part-time doctoral supervisor of Xiangtan University. Director of Sol-Gel Branch of China Silicate Society. The academic leader of the doctoral program of " Material Physics and Chemistry " in the College of Physics and Optoelectronic Engineering, the academic leader of the provincial key advantageous discipline " Electronic Science and Technology, " and the provincial key training object of " Thousands of Projects " in Guangdong Province. For more than ten years, he has been engaged in the basic research work on the physical properties of ferroelectric thin films and ceramics.

Corresponding author: Xingui Tang, xgtang@gdut.edu.cn

DOI: 10.1088/1674-4926/25060032CSTR: 32376.14.1674-4926.25060032

Abstract: In recent years, optoelectronic synapses have garnered significant attention in the field of neuromorphic computing due to their integration of optical sensing and synaptic functions. In this work, we propose an optoelectronic synapse based on IGZO/Bi_3.25La_0.75Ti₃O₁₂ heterojunction. Under UV light stimulation, this device can simulate a range of synaptic behaviors, including paired-pulse facilitation, spike-intensity-dependent plasticity, spike-number-dependent plasticity, spike-width-dependent plasticity, and the transition from short-term memory to long-term memory. The majority of perceptible information for humans is acquired through the visual system. The 3 × 3 retinal morphology synapse arrays constructed based on plasticity behaviors not only integrates light perception and storage functions but also exhibits adaptive adjustment capabilities to address image blurring caused by object movement. At the same time, in CNN recognition training, the device successfully simulates the learning-relearning mechanism of the human brain. These findings highlight the device’s immense potential for applications in artificial vision systems.

Key words: optoelectronic synapses, thin films, neural networks

References

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Fig. 1. (Color online) (a) Schematic diagram of the Au/IGZO/BLTO/ITO optoelectronic synapse structure. (b) TEM image of the cross-section of this device. (c) EDS elemental mapping images of In, Ga, Zn, Bi, La and Ti. (d) HRTEM image of the IGZO/BLTO heterojunction. (e) FFT image of IGZO and BLTO layers.

DownLoad: Full-Size Img PowerPoint

Fig. 2. (Color online) (a) The schematic diagram of the device under the condition of illumination test. (b) Schematic diagram of carrier motion inside the device (under illumination) (c) The optoelectronic response of the device under a single light pulse. (d) The optoelectronic response under two rapid successive light pulses. (e) Variation of PPF index with the time interval. (f) Simulation of the “learning-forgetting-relearning” process (single pulse). (g) Simulation of the “learning-forgetting-relearning” process (multiple pulse).

DownLoad: Full-Size Img PowerPoint

Fig. 3. (Color online) Photoelectronic response of the device under light pulses with (a) different numbers, (b) different power and (c) different widths. (d) Decay of photocurrent under different numbers of pulse. (e) Decay of photocurrent under different power of pulse. (f) Decay of photocurrent under different widths of pulse.

DownLoad: Full-Size Img PowerPoint

Fig. 4. (Color online) (a) Schematic diagram of the learning and memory model illustrating the transition from STM to LTM. (b) Illustration of a 3 × 3 synaptic arrays illuminated through a "T" pattern mask. (c) Gradual clarification of the pattern with increased light stimulation time. (d) Image mapping at different relaxation times after light removal. (e) Image mapping at different relaxation times following unexpected dithering.

DownLoad: Full-Size Img PowerPoint

Fig. 5. (Color online) (a) The output current of the device corresponds to the process of human brain learning-forgetting-relearning. (b) The carrier motion model inside the material under the first light excitation of the device. (c) The carrier motion state inside the device material after the light source is turned off. (d) The carrier motion model inside the material under the second light excitation of the device.

DownLoad: Full-Size Img PowerPoint

Fig. 6. (Color online) (a) CNN neural network training diagram. (b) and (c) Improved weight update method and differential pair model used. (d) The A_p value of the device in the first training. (e) The A_p value of the device in the second training. (f) The recognition rate of CNN handwritten digit set in the case of first learning and relearning of the device (20 iteration cycles).

DownLoad: Full-Size Img PowerPoint

[1]	Feng C, Li B W, Dong Y, et al. Quantum imaging of the reconfigurable VO₂ synaptic electronics for neuromorphic computing. Sci Adv, 2023, 9(40), eadg9376 doi: 10.1126/sciadv.adg9376
[2]	Liu Y B, Cai D, Zhao T C, et al. Monolayer MoS₂ synaptic devices synergistically modulated by Na+ ions and sulfur vacancies for neuromorphic computing and pain perception stimulation. J Mater Sci Technol, 2023, 163, 121 doi: 10.1016/j.jmst.2023.04.025
[3]	Yuan S, Qiu B, Amina K, et al. Robust and low-power-consumption black phosphorus–graphene artificial synaptic devices. ACS Appl. Mater. Interfaces, 2022, 14(18), 21242
[4]	Yan X, Zhao J, Liu S, et al. Memristor with Ag-cluster-doped TiO₂ films as artificial synapse for neuroinspired computing. Adv Funct Mater, 2018, 28(1), 1705320 doi: 10.1002/adfm.201705320
[5]	Li Z, Wang T, Meng J, et al. Flexible aluminum-doped hafnium oxide ferroelectric synapse devices for neuromorphic computing. Mater Horiz, 2023, 10(9), 3643 doi: 10.1039/D3MH00645J
[6]	Guo Z, Liu G, Sun Y, et al. High-performance neuromorphic computing and logic operation based on a self-assembled vertically aligned nanocomposite SrTiO₃: MgO film memristor. ACS Nano, 2023, 17(21), 21518 doi: 10.1021/acsnano.3c06510
[7]	Zhang Y, Zhou R, Ruan S, et al. An efficient MSM UV photodetector with an ultra-low dark current enhanced by a back to back TiO₂/PbTiO₃ PN heterojunction. Appl Phys Lett, 2023, 123(13
[8]	Wu J, Ye W, Lin J, et al. Temperature regulated artificial neuron based on memristor. IEEE Electr Device L, 2022, 43(11), 2001 doi: 10.1109/LED.2022.3206796
[9]	Patil H, Rehman S, Kim H, et al. Effect of growth temperature on self-rectifying BaTiO₃/ZnO heterojunction for high-density crossbar arrays and neuromorphic computing. J Colloid Interf Sci, 2023, 652, 836 doi: 10.1016/j.jcis.2023.08.105
[10]	Li Q, Wang T, Yang Y, et al. Artificial vision adaptation based on optoelectronic neuromorphic transistors. IEEE Electr Device L, 2022, 43(11), 1917 doi: 10.1109/LED.2022.3205315
[11]	Wang S, Chen H, Liu T, et al. Retina-inspired organic photonic synapses for selective detection of SWIR light. Angew Chem Int Edit, 2023, 62(6), e202213733 doi: 10.1002/anie.202213733
[12]	Gong Y, Xie P, Xing X, et al. Bioinspired artificial visual system based on 2D WSe₂ synapse array. Adv Funct Mater, 2023, 33(41), 2303539 doi: 10.1002/adfm.202303539
[13]	Liu X, Wang F, Su J, et al. Bio-inspired 3D artificial neuromorphic circuits. Adv Funct Mater, 2022, 32(22), 2113050 doi: 10.1002/adfm.202113050
[14]	Fan S, Wu E, Cao M, et al. Flexible In–Ga–Zn–N–O synaptic transistors for ultralow-power neuromorphic computing and EEG-based brain–computer interfaces. Mater Horiz, 2023, 10(10), 4317 doi: 10.1039/D3MH00759F
[15]	Wang Y L, Cao M, Bian J, et al. Flexible ZnO nanosheet-based artificial synapses prepared by low-temperature process for high recognition accuracy neuromorphic computing. Adv Funct Mater, 2022, 32(52), 2209907 doi: 10.1002/adfm.202209907
[16]	Wu X, Shi S, Liang B, et al. Ultralow-power optoelectronic synaptic transistors based on polyzwitterion dielectrics for in-sensor reservoir computing. Sci Adv, 2024, 10(16), eadn4524 doi: 10.1126/sciadv.adn4524
[17]	Fang Y, Meng J, Li Q, et al. Two-terminal photoelectric dual modulation synaptic devices for face recognition. IEEE Electr Device L, 2022, 44(2), 241
[18]	Li R, Wang W, Li Y, et al. Multi-modulated optoelectronic memristor based on Ga₂O₃/MoS₂ heterojunction for bionic synapses and artificial visual system. Nano Energy, 2023, 111, 108398 doi: 10.1016/j.nanoen.2023.108398
[19]	Zhang Y G, Zheng H W, Zhang J X, et al. Photovoltaic effects in Bi₄Ti₃O₁₂ thin film prepared by a sol–gel method. Mater Lett, 2014, 125, 25 doi: 10.1016/j.matlet.2014.03.146
[20]	Casadio S, Gondolini A, Sangiorgi N, et al. Highly transparent Bi₄Ti₃O₁₂ thin-film electrodes for ferroelectric-enhanced photoelectrochemical processes. Sustain Energ Fuels, 2023, 7(4), 1107 doi: 10.1039/D2SE01372J
[21]	Jiang Y P, Zhou H C, Tang X G, et al. Enhanced and controllable ferroelectric photovoltaic effects in Bi₄Ti₃O₁₂/TiO₂ composite films. J Electron Mater, 2023, 52(1), 188 doi: 10.1007/s11664-022-09960-6
[22]	Lu X, Wu X, Li L, et al. Switching properties of Nd-and La-doped Bi₄Ti₃O₁₂ thin films under applied stress. Phys Rev B—Condens Matt and Mater Phys, 2005, 72(21), 212103 doi: 10.1103/PhysRevB.72.212103
[23]	Garg A, Hu X, Barber Z H. Structure and properties of lanthanide doped Bi₄Ti₃O₁₂ films. Ferroelectrics, 2005, 328(1), 93 doi: 10.1080/00150190500311292
[24]	Le B Q, Van T N, Quang D T, et al. A simple and effective carrier lifetime characterization for semiconductor thin films. J Semicond, 2025, 46(7), 072101-1-072101-12 doi: 10.1088/1674-4926/24090005
[25]	Zhu Y, Mao H, Zhu Y, et al. Photoelectric synapse based on InGaZnO nanofibers for high precision neuromorphic computing. IEEE Elect Device L, 2022, 43(4), 651-654 doi: 10.1109/LED.2022.3149900
[26]	Yang Y, Cui H, Ke S, et al. Reservoir computing based on electric-double-layer coupled InGaZnO artificial synapse. Appl Phys Lett, 2023, 122(4
[27]	Wang Z Q, Xu H Y, Li X H, et al. Synaptic learning and memory functions achieved using oxygen ion migration/diffusion in an amorphous InGaZnO memristor. Adv Funct Mater, 2012, 22(13), 2759 doi: 10.1002/adfm.201103148
[28]	Ghafoor F, Kim H, Ghafoor B, et al. Interface engineering in ZnO/CdO hybrid nanocomposites to enhanced resistive switching memory for neuromorphic computing. J Colloid Interf Sci, 2024, 659, 1 doi: 10.1016/j.jcis.2023.12.084
[29]	Jiang S, Peng L, Li L, et al. Task-adaptive neuromorphic computing using reconfigurable organic neuristors with tunable plasticity and logic-in-memory operations. J Phys Chem Lett, 2024, 15(9), 2301 doi: 10.1021/acs.jpclett.4c00284
[30]	Zhou G, Li J, Song Q, et al. Full hardware implementation of neuromorphic visual system based on multimodal optoelectronic resistive memory arrays for versatile image processing. Nat Commun, 2023, 14(1), 8489 doi: 10.1038/s41467-023-43944-2
[31]	Wang Y, Lv Z, Chen J, et al. Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv mater, 2018, 30(38), 1802883 doi: 10.1002/adma.201802883
[32]	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(1), 104 doi: 10.1007/s40820-024-01330-7
[33]	Chen L, Li R, Yuan S, et al. Fiber-shaped artificial optoelectronic synapses for wearable visual-memory systems. Matter, 2023, 6(3), 925 doi: 10.1016/j.matt.2022.12.001
[34]	Akbari M K, Ramachandran R K, Detavernier C, et al. Heterostructured plasmonic memristors with tunable opto-synaptic functionalities. J Mater Chem C, 2021, 9(7), 2539 doi: 10.1039/D0TC05563H
[35]	Liu S, Wu Z, He Z, et al. Low-power perovskite neuromorphic synapse with enhanced photon efficiency for directional motion perception. ACS Appl Mater Interfaces, 2024, 16(17), 22303 doi: 10.1021/acsami.4c04398
[36]	Wu T, Gao S, Li Y. IGZO/WO₃− x-Heterostructured Artificial Optoelectronic Synaptic Devices Mimicking Image Segmentation and Motion Capture. Small, 2024, 20(27), 2309857 doi: 10.1002/smll.202309857
[37]	Li Q, Wang T, Fang Y, et al. Ultralow power wearable organic ferroelectric device for optoelectronic neuromorphic computing. Nano Lett, 2022, 22(15), 6435 doi: 10.1021/acs.nanolett.2c01768
[38]	Dong L, Yuan S, Wei G, et al. Artificial optoelectronic synapse based on violet phosphorus microfiber arrays. Small, 2024, 20(13), 2306998 doi: 10.1002/smll.202306998
[39]	Xu L, Wang W, Li Y, et al. N: ZnO/MoS₂-heterostructured flexible synaptic devices enabling optoelectronic co-modulation for robust artificial visual systems. Nano Res, 2024, 17(3), 1902 doi: 10.1007/s12274-023-6004-4
[40]	Wan Z, Zhang Q, Hu F, et al. Topological insulator optoelectronic synapses for high-accuracy binary image recognition using recurrent neural networks. Adv Opt Mater, 2023, 11(2), 2201852 doi: 10.1002/adom.202201852
[41]	Liang J, Yu X, Qiu J, et al. All-optically controlled artificial synapses based on light-induced adsorption and desorption for neuromorphic vision. ACS Appl Mater Interfaces, 2023, 15(7), 9584 doi: 10.1021/acsami.2c20166
[42]	Yang L, Singh M, Shen S W, et al. Transparent and flexible inorganic perovskite photonic artificial synapses with dual-mode operation. Adv Funct Mater, 2021, 31(6), 2008259 doi: 10.1002/adfm.202008259
[43]	Yang D P, Li R H, Tang X G, et al. Photoelectric dual mode sensing system based on one-step fabricated heterojunction artificial synapses device. Mater Sci Eng R R, 2025, 165, 101021 doi: 10.1016/j.mser.2025.101021
[44]	Li D L, Tang X G, Zhong W M, et al. Photoelectric dual-mode and nonvolatile modulation based on ferroelectric memristors for reservoir computing. ACS Sustainable Chem & Eng, 2024, 12(35), 13361
[45]	Fang J, Tang Z, Lai X C, et al. New-style logic operation and neuromorphic computing enabled by optoelectronic artificial synapses in an MXene/Y: HfO₂ ferroelectric memristor. ACS Appl Mater Interfaces, 2024, 16(24), 31348 doi: 10.1021/acsami.4c05316
[46]	Chen R, Jia X, Zhou H, et al. Applications of MXenes in wearable sensing: Advances, challenges, and prospects. Mater Today, 2024, 75, 359 doi: 10.1016/j.mattod.2024.04.013
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Received: Revised: Online: Accepted Manuscript: 13 August 2025

Article Navigation > Journal of Semiconductors > 2025 > Accepted Manuscript

Dongping Yang, Hao Chen, Zhenhua Tang, Qijun Sun, Xingui Tang. Optoelectronic synapses based on IGZO/Bi3.25La0.75Ti3O12 heterojunctions for human brain learning mechanism simulation[J]. Journal of Semiconductors, 2025, In Press. doi: 10.1088/1674-4926/25060032 ****D P Yang, H Chen, Z H Tang, Q J Sun, and X G Tang, Optoelectronic synapses based on IGZO/Bi3.25La0.75Ti3O12 heterojunctions for human brain learning mechanism simulation[J]. J. Semicond., 2025, accepted doi: 10.1088/1674-4926/25060032

Citation:

Dongping Yang, Hao Chen, Zhenhua Tang, Qijun Sun, Xingui Tang. Optoelectronic synapses based on IGZO/Bi₃_.₂₅La₀_.₇₅Ti₃O₁₂ heterojunctions for human brain learning mechanism simulation[J]. Journal of Semiconductors, 2025, In Press. doi: 10.1088/1674-4926/25060032 ****

D P Yang, H Chen, Z H Tang, Q J Sun, and X G Tang, Optoelectronic synapses based on IGZO/Bi₃_.₂₅La₀_.₇₅Ti₃O₁₂ heterojunctions for human brain learning mechanism simulation[J]. J. Semicond., 2025, accepted doi: 10.1088/1674-4926/25060032

Citation:

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Optoelectronic synapses based on IGZO/Bi₃_.₂₅La₀_.₇₅Ti₃O₁₂ heterojunctions for human brain learning mechanism simulation

DOI: 10.1088/1674-4926/25060032

CSTR: 32376.14.1674-4926.25060032

School of Physics and Optoelectric Engineering & Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangdong University of Technology, Guangzhou 510006, China

More Information

Dongping Yang：Dong-Ping Yang received his M.S. degree in Electronic Information from Guangdong University of Technology (GDUT), China, in 2023 under the supervision of Prof. Miao He. He is currently pursuing a Ph.D. in Material Physics and Chemistry at GDUT, co-advised by Prof. Xin-Gui Tang and Prof. Qi-Jun Sun. His research focuses on the development of photoelectric synaptic devices for artificial neural network applications. He specializes in thin-film fabrication and characterization, optoelectronic pulse modulation techniques, and neural networkbased pattern recognition training. Dr. Yang has authored 3 first-author SCI papers (6 publications in total) on neuromorphic devices and artificial intelligence hardware systems. His current research interests include neuromorphic computing, brain-inspired intelligent systems, and the integration of optoelectronic materials with machine learning algorithms
Xingui Tang：Xin-Gui Tang, doctoral supervisor of Guangdong University of Technology, part-time doctoral supervisor of Xiangtan University. Director of Sol-Gel Branch of China Silicate Society. The academic leader of the doctoral program of " Material Physics and Chemistry " in the College of Physics and Optoelectronic Engineering, the academic leader of the provincial key advantageous discipline " Electronic Science and Technology, " and the provincial key training object of " Thousands of Projects " in Guangdong Province. For more than ten years, he has been engaged in the basic research work on the physical properties of ferroelectric thin films and ceramics
Corresponding author: xgtang@gdut.edu.cn
Available Online: 2025-08-13

Abstract

Abstract

In recent years, optoelectronic synapses have garnered significant attention in the field of neuromorphic computing due to their integration of optical sensing and synaptic functions. In this work, we propose an optoelectronic synapse based on IGZO/Bi_3.25La_0.75Ti₃O₁₂ heterojunction. Under UV light stimulation, this device can simulate a range of synaptic behaviors, including paired-pulse facilitation, spike-intensity-dependent plasticity, spike-number-dependent plasticity, spike-width-dependent plasticity, and the transition from short-term memory to long-term memory. The majority of perceptible information for humans is acquired through the visual system. The 3 × 3 retinal morphology synapse arrays constructed based on plasticity behaviors not only integrates light perception and storage functions but also exhibits adaptive adjustment capabilities to address image blurring caused by object movement. At the same time, in CNN recognition training, the device successfully simulates the learning-relearning mechanism of the human brain. These findings highlight the device’s immense potential for applications in artificial vision systems.
- optoelectronic synapses,
- thin films,
- neural networks

^§Dongping Yang and Hao Chen contributed equally to this work and should be considered co-first authors.

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