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Vol. 17 (2025)

Low Energy Consumption Photoelectric Memristors with Multi-Level Linear Conductance Modulation in Artificial Visual Systems Application

https://doi.org/10.1007/s40820-025-01816-y
Zhenyu Zhou
College of Electron and Information Engineering, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain‑Like Neuromorphic Devices and, Systems of Hebei Province, Hebei University, Baoding 071002, People’s Republic of China
Zixuan Zhang
College of Electron and Information Engineering, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain‑Like Neuromorphic Devices and, Systems of Hebei Province, Hebei University, Baoding 071002, People’s Republic of China
Pengfei Li
College of Electron and Information Engineering, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain‑Like Neuromorphic Devices and, Systems of Hebei Province, Hebei University, Baoding 071002, People’s Republic of China
Zhiyuan Guan
College of Electron and Information Engineering, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain‑Like Neuromorphic Devices and, Systems of Hebei Province, Hebei University, Baoding 071002, People’s Republic of China
Yuchen Li
College of Electron and Information Engineering, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain‑Like Neuromorphic Devices and, Systems of Hebei Province, Hebei University, Baoding 071002, People’s Republic of China
Xiaoxu Li
College of Electron and Information Engineering, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain‑Like Neuromorphic Devices and, Systems of Hebei Province, Hebei University, Baoding 071002, People’s Republic of China
Shan Xu
College of Electron and Information Engineering, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain‑Like Neuromorphic Devices and, Systems of Hebei Province, Hebei University, Baoding 071002, People’s Republic of China
Jianhui Zhao
College of Electron and Information Engineering, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain‑Like Neuromorphic Devices and, Systems of Hebei Province, Hebei University, Baoding 071002, People’s Republic of China
Xiaobing Yan
College of Electron and Information Engineering, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain‑Like Neuromorphic Devices and, Systems of Hebei Province, Hebei University, Baoding 071002, People’s Republic of China

Corresponding Author: Xiaobing Yan

yanxiaobing@ime.ac.cn

Nano-Micro Letters, Vol. 17 (2025), Article Number: 317

Abstract

Optical synapses have an ability to perceive and remember visual information, making them expected to provide more intelligent and efficient visual solutions for humans. As a new type of artificial visual sensory devices, photoelectric memristors can fully simulate synaptic performance and have great prospects in the development of biological vision. However, due to the urgent problems of nonlinear conductance and high-energy consumption, its further application in high-precision control scenarios and integration is hindered. In this work, we report an optoelectronic memristor with a structure of TiN/CeO2/ZnO/ITO/Mica, which can achieve minimal energy consumption (187 pJ) at a single pulse (0.5 V, 5 ms). Under the stimulation of continuous pulses, linearity can be achieved up to 99.6%. In addition, the device has a variety of synaptic functions under the combined action of photoelectric, which can be used for advanced vision. By utilizing its typical long-term memory characteristics, we achieved image recognition and long-term memory in a 3 × 3 synaptic array and further achieved female facial feature extraction behavior with an activation rate of over 92%. Moreover, we also use the linear response characteristic of the device to design and implement the night meeting behavior of autonomous vehicles based on the hardware platform. This work highlights the potential of photoelectric memristors for advancing neuromorphic vision systems, offering a new direction for bionic eyes and visual automation technology.


Highlights:
1 Developed a novel photo-memristor with single-pulse low energy consumption (187 pJ) and multi-pulse linearity up to 0.996.
2 Using photoelectric synaptic characteristics, achieved long-term memory in a 3 × 3 array and over 92% activation of female facial feature recognition in a 64 × 64 model.
3 Using the continuous response characteristics of optical synapses, an intelligent driving system with automatic night meeting was designed.

Keywords

Photoelectric memristors Optical synapses Low energy Linear response Intelligent drive
Zhou, Zhenyu, Zixuan Zhang, Pengfei Li, Zhiyuan Guan, Yuchen Li, Xiaoxu Li, Shan Xu, Jianhui Zhao, and Xiaobing Yan. 2025. “Low Energy Consumption Photoelectric Memristors With Multi-Level Linear Conductance Modulation in Artificial Visual Systems Application”. Nano-Micro Letters 17 (July):317. https://doi.org/10.1007/s40820-025-01816-y.
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References
  1. B.H. Jeong, J. Lee, M. Ku, J. Lee, D. Kim et al., RGB color-discriminable photonic synapse for neuromorphic vision system. Nano-Micro Lett. 17(1), 78 (2024). https://doi.org/10.1007/s40820-024-01579-y
  2. K. Liu, T. Zhang, B. Dang, L. Bao, L. Xu et al., An optoelectronic synapse based on α-In2Se3 with controllable temporal dynamics for multimode and multiscale reservoir computing. Nat. Electron. 5(11), 761–773 (2022). https://doi.org/10.1038/s41928-022-00847-2
  3. S. Zhong, L. Su, M. Xu, D. Loke, B. Yu et al., Recent advances in artificial sensory neurons: biological fundamentals, devices, applications, and challenges. Nano-Micro Lett. 17(1), 61 (2024). https://doi.org/10.1007/s40820-024-01550-x
  4. B. Dang, T. Zhang, X. Wu, K. Liu, R. Huang et al., Reconfigurable in-sensor processing based on a multi-phototransistor–one-memristor array. Nat. Electron. 7(11), 991–1003 (2024). https://doi.org/10.1038/s41928-024-01280-3
  5. X. Ji, B.D. Paulsen, G.K.K. Chik, R. Wu, Y. Yin et al., Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor. Nat. Commun. 12(1), 2480 (2021). https://doi.org/10.1038/s41467-021-22680-5
  6. Y. van de Burgt, E. Lubberman, E.J. Fuller, S.T. Keene, G.C. Faria et al., A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16(4), 414–418 (2017). https://doi.org/10.1038/nmat4856
  7. G.-X. Zhang, Z.-C. Zhang, X.-D. Chen, L. Kang, Y. Li et al., Broadband sensory networks with locally stored responsivities for neuromorphic machine vision. Sci. Adv. 9(37), eadi5104 (2023). https://doi.org/10.1126/sciadv.adi5104
  8. Z. Long, X. Qiu, C.L.J. Chan, Z. Sun, Z. Yuan et al., A neuromorphic bionic eye with filter-free color vision using hemispherical perovskite nanowire array retina. Nat. Commun. 14(1), 1972 (2023). https://doi.org/10.1038/s41467-023-37581-y
  9. Z. Zhang, X. Zhao, X. Zhang, X. Hou, X. Ma et al., In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array. Nat. Commun. 13(1), 6590 (2022). https://doi.org/10.1038/s41467-022-34230-8
  10. G. Wu, X. Zhang, G. Feng, J. Wang, K. Zhou et al., Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing. Nat. Mater. 22(12), 1499–1506 (2023). https://doi.org/10.1038/s41563-023-01676-0
  11. J. Chen, Z. Zhou, B.J. Kim, Y. Zhou, Z. Wang et al., Optoelectronic graded neurons for bioinspired in-sensor motion perception. Nat. Nanotechnol. 18(8), 882–888 (2023). https://doi.org/10.1038/s41565-023-01379-2
  12. X. Wu, S. Wang, W. Huang, Y. Dong, Z. Wang et al., Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning. Nat. Commun. 14(1), 468 (2023). https://doi.org/10.1038/s41467-023-36205-9
  13. X. Yang, Z. Xiong, Y. Chen, Y. Ren, L. Zhou et al., A self-powered artificial retina perception system for image preprocessing based on photovoltaic devices and memristive arrays. Nano Energy 78, 105246 (2020). https://doi.org/10.1016/j.nanoen.2020.105246
  14. Y. Lee, T.-W. Lee, Organic synapses for neuromorphic electronics: from brain-inspired computing to sensorimotor nervetronics. Acc. Chem. Res. 52(4), 964–974 (2019). https://doi.org/10.1021/acs.accounts.8b00553
  15. C. Wan, P. Cai, M. Wang, Y. Qian, W. Huang et al., Artificial sensory memory. Adv. Mater. 32(15), 1902434 (2020). https://doi.org/10.1002/adma.201902434
  16. S. Zhu, T. Xie, Z. Lv, Y.-B. Leng, Y.-Q. Zhang et al., Hierarchies in visual pathway: functions and inspired artificial vision. Adv. Mater. 36(6), 2301986 (2024). https://doi.org/10.1002/adma.202301986
  17. J. Lee, B.H. Jeong, E. Kamaraj, D. Kim, H. Kim et al., Light-enhanced molecular polarity enabling multispectral color-cognitive memristor for neuromorphic visual system. Nat. Commun. 14(1), 5775 (2023). https://doi.org/10.1038/s41467-023-41419-y
  18. C. Zhu, H. Liu, W. Wang, L. Xiang, J. Jiang et al., Optical synaptic devices with ultra-low power consumption for neuromorphic computing. Light Sci. Appl. 11(1), 337 (2022). https://doi.org/10.1038/s41377-022-01031-z
  19. M. Lian, C. Gao, Z. Lin, L. Shan, C. Chen et al., Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output. Light Sci. Appl. 13(1), 179 (2024). https://doi.org/10.1038/s41377-024-01516-z
  20. F. Cao, Z. Hu, T. Yan, E. Hong, X. Deng et al., A dual-functional perovskite-based photodetector and memristor for visual memory. Adv. Mater. 35(44), e2304550 (2023). https://doi.org/10.1002/adma.202304550
  21. Y. Lin, W. Wang, R. Li, J. Kim, C. Zhang et al., Multifunctional optoelectronic memristor based on CeO2/MoS2 heterojunction for advanced artificial synapses and bionic visual system with nociceptive sensing. Nano Energy 121, 109267 (2024). https://doi.org/10.1016/j.nanoen.2024.109267
  22. T.-Y. Wang, J.-L. Meng, Q.-X. Li, Z.-Y. He, H. Zhu et al., Reconfigurable optoelectronic memristor for in-sensor computing applications. Nano Energy 89, 106291 (2021). https://doi.org/10.1016/j.nanoen.2021.106291
  23. Z. Zhao, Z. Wang, J. Xu, P. Zhao, J. Wang et al., High photoresponsivity and fast response speed ferroelectric photomemristor for artificial visual system application. Adv. Funct. Mater. 34(45), 2406666 (2024). https://doi.org/10.1002/adfm.202406666
  24. S. Feng, J. Li, L. Feng, Z. Liu, J. Wang et al., Dual-mode conversion of photodetector and neuromorphic vision sensor via bias voltage regulation on a single device. Adv. Mater. 35(49), 2308090 (2023). https://doi.org/10.1002/adma.202308090
  25. X. Duan, Z. Cao, K. Gao, W. Yan, S. Sun et al., Memristor-based neuromorphic chips. Adv. Mater. 36(14), 2310704 (2024). https://doi.org/10.1002/adma.202310704
  26. J. Zeng, B. Zhao, Y. Liu, T. Xu, W. Jiang et al., Linear weight update synaptic responses in ferrimagnetic neuromorphic devices. Adv. Electron. Mater. 11(5), 2400591 (2025). https://doi.org/10.1002/aelm.202400591
  27. S.-O. Park, T. Park, H. Jeong, S. Hong, S. Seo et al., Linear conductance update improvement of CMOS-compatible second-order memristors for fast and energy-efficient training of a neural network using a memristor crossbar array. Nanoscale Horiz. 8(10), 1366–1376 (2023). https://doi.org/10.1039/D3NH00121K
  28. K. He, C. Wang, Y. He, J. Su, X. Chen, Artificial neuron devices. Chem. Rev. 123(23), 13796–13865 (2023). https://doi.org/10.1021/acs.chemrev.3c00527
  29. H. Tan, G. Liu, X. Zhu, H. Yang, B. Chen et al., An optoelectronic resistive switching memory with integrated demodulating and arithmetic functions. Adv. Mater. 27(17), 2797–2803 (2015). https://doi.org/10.1002/adma.201500039
  30. H. Tan, G. Liu, H. Yang, X. Yi, L. Pan et al., Light-gated memristor with integrated logic and memory functions. ACS Nano 11(11), 11298–11305 (2017). https://doi.org/10.1021/acsnano.7b05762
  31. J. Jiang, W. Xiao, X. Li, Y. Zhao, Z. Qin et al., Hardware-level image recognition system based on ZnO photo-synapse array with the self-denoising function. Adv. Funct. Mater. 34(19), 2313507 (2024). https://doi.org/10.1002/adfm.202313507
  32. G. Indiveri, E. Chicca, R. Douglas, A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans. Neural Netw. 17(1), 211–221 (2006). https://doi.org/10.1109/TNN.2005.860850
  33. V. Krishnamurthi, T. Ahmed, M. Mohiuddin, A. Zavabeti, N. Pillai et al., A visible-blind photodetector and artificial optoelectronic synapse using liquid-metal exfoliated ZnO nanosheets. Adv. Opt. Mater. 9(16), 2100449 (2021). https://doi.org/10.1002/adom.202100449
  34. Y. Sun, L. Qian, D. Xie, Y. Lin, M. Sun et al., Photoelectric synaptic plasticity realized by 2D perovskite. Adv. Funct. Mater. 29(28), 1902538 (2019). https://doi.org/10.1002/adfm.201902538
  35. P. Li, M. Zhang, Q. Zhou, Q. Zhang, D. Xie et al., Reconfigurable optoelectronic transistors for multimodal recognition. Nat. Commun. 15(1), 3257 (2024). https://doi.org/10.1038/s41467-024-47580-2
  36. Y. Wang, Z. Lv, J. Chen, Z. Wang, Y. Zhou et al., Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv. Mater. 30(38), 1802883 (2018). https://doi.org/10.1002/adma.201802883
  37. J. Zeng, Y. Chen, J. Liu, T. Xu, L. Fang et al., Ferrimagnet-based neuromorphic device mimicking the ventral visual pathway for high-accuracy target recognition. ACS Appl. Mater. Interfaces 16(43), 59088–59095 (2024). https://doi.org/10.1021/acsami.4c13405
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