用于光学神经形态计算的人工电致变色突触
Artificial Electrochromic Synapses for Optical Neuromorphic Computing
DOI: 10.12677/mos.2025.145423, PDF,   
作者: 刘成宇, 陈 希*：上海理工大学智能科技学院，上海；上海理工大学光子芯片研究院，上海
关键词: 人工突触；电致发光；衍射深度神经网络；盲文识别；Artificial Synapse； Electrochromic； Diffractive Deep Neural Network； Braille Recognition
摘要: 光学神经形态计算具有速度快、并行处理、容量大、抗干扰能力强等优点，因此在视觉感知、智能语音系统和生物医学图像处理方面大有可为。然而，对于触觉、嗅觉和听觉等感官来说，从感官输入转换而来的电反应无法输入到光学神经形态计算的框架中。如何实现光电转换是光学神经形态计算面临的巨大挑战。文章提出了一种基于人工电致变色突触的电光转换途径，由普鲁士蓝(Prussian Blue, PB)和氧化钨(Tungsten Oxide, WO3)组成的突触在不同电压下表现出可调的光学透射率，根据电压脉冲下的透射率响应，可以展示典型的突触行为，包括短期记忆、长期记忆和双脉冲易化。然后制作了一个3 × 4的人工电致变色突触阵列，用于显示盲文数字图像。为了进行盲文识别，图像被输入到一个衍射深度神经网络(Diffraction Deep Neural Network, D2NN)，这是一个结合了光学衍射原理和深度学习的光学神经形态计算框架。实验显示盲文数字0~9的识别准确率达到100%。这些结果表明，将人工电致变色突触与光学神经形态计算相结合，为实现低成本、快速、高效的多模态人工智能系统提供了新的方向。
Abstract: Optical neuromorphic computing offers advantages such as high speed, parallel processing, large capacity, and strong resistance to interference, making it promising for visual perception, intelligent speech systems, and biomedical image processing. However, for sensories of tactility, olfactory, and auditory, electrical responses converted from sensory inputs cannot be inputted into frameworks of optical neuromorphic computing. It is a tremendous challenge for optical neuromorphic computing to achieve electrical-optical conversion. In this paper, a pathway of electrical-optical conversion based on artificial electrochromic synapses is proposed. The synapse, consisting of Prussian blue and tungsten oxide, exhibits tunable optical transmittances under different voltages. Based on the transmittance responses under voltage pulses, typical synaptic behaviors, including short-term memory, long-term memory, and paired-pulse facilitation, can be demonstrated. Next, a 3 × 4 array of artificial electrochromic synapses is fabricated to display images of Braille numbers. For Braille recognition, the images are input to a diffractive deep neural network, an optical neuromorphic computing framework combining the principle of optical diffraction with deep learning. The recognition accuracy of Braille numbers 0~9 reaches 100%. The results imply that integrating artificial electrochromic synapses with optical neuromorphic computing provides a new direction for realizing low-cost, fast, and efficient multimodal artificial intelligence systems.
文章引用：刘成宇, 陈希. 用于光学神经形态计算的人工电致变色突触[J]. 建模与仿真, 2025, 14(5): 659-669. https://doi.org/10.12677/mos.2025.145423

参考文献

[1] Cai, H., Ao, Z., Tian, C., Wu, Z., Liu, H., Tchieu, J., et al. (2023) Brain Organoid Reservoir Computing for Artificial Intelligence. Nature Electronics, 6, 1032-1039. [Google Scholar] [CrossRef] 
[2] Murphy, R.R. (2022) Would You Trust an Intelligent Robot? Science Robotics, 7, eade0862. [Google Scholar] [CrossRef] 
[3] Wang, L., Zhang, X., Li, Q., Zhang, M., Su, H., Zhu, J., et al. (2023) Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence. Nature Machine Intelligence, 5, 1356-1368. [Google Scholar] [CrossRef] 
[4] Li, C., Hu, M., Li, Y., Jiang, H., Ge, N., Montgomery, E., et al. (2017) Analogue Signal and Image Processing with Large Memristor Crossbars. Nature Electronics, 1, 52-59. [Google Scholar] [CrossRef] 
[5] Liu, L., Xu, W., Ni, Y., Xu, Z., Cui, B., Liu, J., et al. (2022) Stretchable Neuromorphic Transistor That Combines Multisensing and Information Processing for Epidermal Gesture Recognition. ACS Nano, 16, 2282-2291. [Google Scholar] [CrossRef] [PubMed]
[6] Li, C., Belkin, D., Li, Y., Yan, P., Hu, M., Ge, N., et al. (2018) Efficient and Self-Adaptive In-Situ Learning in Multilayer Memristor Neural Networks. Nature Communications, 9, Article No. 2385. [Google Scholar] [CrossRef] [PubMed]
[7] Li, R., Dong, Y., Qian, F., Xie, Y., Chen, X., Zhang, Q., et al. (2023) CsPbBr3/Graphene Nanowall Artificial Optoelectronic Synapses for Controllable Perceptual Learning. PhotoniX, 4, Article No. 4. [Google Scholar] [CrossRef] 
[8] Wang, R., Chen, P., Hao, D., Zhang, J., Shi, Q., Liu, D., et al. (2021) Artificial Synapses Based on Lead-Free Perovskite Floating-Gate Organic Field-Effect Transistors for Supervised and Unsupervised Learning. ACS Applied Materials & Interfaces, 13, 43144-43154. [Google Scholar] [CrossRef] [PubMed]
[9] Zhang, W., Yao, P., Gao, B., Liu, Q., Wu, D., Zhang, Q., et al. (2023) Edge Learning Using a Fully Integrated Neuro-Inspired Memristor Chip. Science, 381, 1205-1211. [Google Scholar] [CrossRef] [PubMed]
[10] Cui, B., Fan, Z., Li, W., Chen, Y., Dong, S., Tan, Z., et al. (2022) Ferroelectric Photosensor Network: An Advanced Hardware Solution to Real-Time Machine Vision. Nature Communications, 13, Article No. 1707. [Google Scholar] [CrossRef] [PubMed]
[11] Liang, J., Yu, X., Qiu, J., Wang, M., Cheng, C., Huang, B., et al. (2023) All-Optically Controlled Artificial Synapses Based on Light-Induced Adsorption and Desorption for Neuromorphic Vision. ACS Applied Materials & Interfaces, 15, 9584-9592. [Google Scholar] [CrossRef] [PubMed]
[12] Sabesan, R., Schmidt, B.P., Tuten, W.S. and Roorda, A. (2016) The Elementary Representation of Spatial and Color Vision in the Human Retina. Science Advances, 2, e1600797. [Google Scholar] [CrossRef] [PubMed]
[13] Shan, X., Zhao, C., Wang, X., Wang, Z., Fu, S., Lin, Y., et al. (2021) Plasmonic Optoelectronic Memristor Enabling Fully Light‐Modulated Synaptic Plasticity for Neuromorphic Vision. Advanced Science, 9, Article ID: 2104632. [Google Scholar] [CrossRef] [PubMed]
[14] Zhu, Q., Li, B., Yang, D., Liu, C., Feng, S., Chen, M., et al. (2021) A Flexible Ultrasensitive Optoelectronic Sensor Array for Neuromorphic Vision Systems. Nature Communications, 12, Article No. 1798. [Google Scholar] [CrossRef] [PubMed]
[15] Han, J., Yun, S., Yu, J., Jeon, S. and Choi, Y. (2023) Artificial Multisensory Neuron with a Single Transistor for Multimodal Perception through Hybrid Visual and Thermal Sensing. ACS Applied Materials & Interfaces, 15, 5449-5455. [Google Scholar] [CrossRef] [PubMed]
[16] Kwon, S.M., Cho, S.W., Kim, M., Heo, J.S., Kim, Y. and Park, S.K. (2019) Environment‐Adaptable Artificial Visual Perception Behaviors Using a Light‐adjustable Optoelectronic Neuromorphic Device Array. Advanced Materials, 31, Article ID: 1906433. [Google Scholar] [CrossRef] [PubMed]
[17] Wang, H., Zhao, Q., Ni, Z., Li, Q., Liu, H., Yang, Y., et al. (2018) A Ferroelectric/Electrochemical Modulated Organic Synapse for Ultraflexible, Artificial Visual‐perception System. Advanced Materials, 30, Article ID: 1803961. [Google Scholar] [CrossRef] [PubMed]
[18] Yoo, H., Kim, E., Chung, J.W., Cho, H., Jeong, S., Kim, H., et al. (2022) Silent Speech Recognition with Strain Sensors and Deep Learning Analysis of Directional Facial Muscle Movement. ACS Applied Materials & Interfaces, 14, 54157-54169. [Google Scholar] [CrossRef] [PubMed]
[19] Cortacero, K., McKenzie, B., Müller, S., Khazen, R., Lafouresse, F., Corsaut, G., et al. (2023) Evolutionary Design of Explainable Algorithms for Biomedical Image Segmentation. Nature Communications, 14, Article No. 7112. [Google Scholar] [CrossRef] [PubMed]
[20] Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J. and Maier-Hein, K.H. (2020) NNU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation. Nature Methods, 18, 203-211. [Google Scholar] [CrossRef] [PubMed]
[21] Markram, H., Muller, E., Ramaswamy, S., Reimann, M.W., Abdellah, M., Sanchez, C.A., et al. (2015) Reconstruction and Simulation of Neocortical Microcircuitry. Cell, 163, 456-492. [Google Scholar] [CrossRef] [PubMed]
[22] Zhou, M., Zhao, Y., Gu, X., Zhang, Q., Zhang, J., Jiang, M., et al. (2023) Realize Low-Power Artificial Photonic Synapse Based on (Al, Ga)n Nanowire/Graphene Heterojunction for Neuromorphic Computing. APL Photonics, 8, Article ID: 076107. [Google Scholar] [CrossRef] 
[23] Liu, J., Wu, Q., Sui, X., Chen, Q., Gu, G., Wang, L., et al. (2021) Research Progress in Optical Neural Networks: Theory, Applications and Developments. PhotoniX, 2, Article No. 5. [Google Scholar] [CrossRef] 
[24] Lin, X., Rivenson, Y., Yardimci, N.T., Veli, M., Luo, Y., Jarrahi, M., et al. (2018) All-Optical Machine Learning Using Diffractive Deep Neural Networks. Science, 361, 1004-1008. [Google Scholar] [CrossRef] [PubMed]
[25] Goi, E., Chen, X., Zhang, Q., Cumming, B.P., Schoenhardt, S., Luan, H., et al. (2021) Nanoprinted High-Neuron-Density Optical Linear Perceptrons Performing Near-Infrared Inference on a CMOS Chip. Light: Science & Applications, 10, Article No. 40. [Google Scholar] [CrossRef] [PubMed]
[26] Cheng, J., Huang, C., Zhang, J., Wu, B., Zhang, W., Liu, X., et al. (2024) Multimodal Deep Learning Using On-Chip Diffractive Optics with in Situ Training Capability. Nature Communications, 15, Article No. 6189. [Google Scholar] [CrossRef] [PubMed]
[27] Lee, Y. and Ahn, J. (2020) Biomimetic Tactile Sensors Based on Nanomaterials. ACS Nano, 14, 1220-1226. [Google Scholar] [CrossRef] [PubMed]
[28] Yang, J., Ge, C., Du, J., Huang, H., He, M., Wang, C., et al. (2018) Artificial Synapses Emulated by an Electrolyte‐Gated Tungsten‐Oxide Transistor. Advanced Materials, 30, Article ID: 1801548. [Google Scholar] [CrossRef] [PubMed]
[29] Wang, Y., Zheng, Y., Gao, J., Jin, T., Li, E., Lian, X., et al. (2021) Band‐tailored Van Der Waals Heterostructure for Multilevel Memory and Artificial Synapse. InfoMat, 3, 917-928. [Google Scholar] [CrossRef] 
[30] Du, J., Ge, C., Riahi, H., Guo, E., He, M., Wang, C., et al. (2020) Dual‐Gated MoS2 Transistors for Synaptic and Programmable Logic Functions. Advanced Electronic Materials, 6, Article ID: 1901408. [Google Scholar] [CrossRef] 
[31] Yang, Q., Yang, H., Lv, D., Yu, R., Li, E., He, L., et al. (2021) High-Performance Organic Synaptic Transistors with an Ultrathin Active Layer for Neuromorphic Computing. ACS Applied Materials & Interfaces, 13, 8672-8681. [Google Scholar] [CrossRef] [PubMed]
[32] Isfahani, V.B., Memarian, N., Dizaji, H.R., Arab, A. and Silva, M.M. (2019) The Physical and Electrochromic Properties of Prussian Blue Thin Films Electrodeposited on ITO Electrodes. Electrochimica Acta, 304, 282-291. [Google Scholar] [CrossRef] 
[33] Qian, J., Ma, D., Xu, Z., Li, D. and Wang, J. (2018) Electrochromic Properties of Hydrothermally Grown Prussian Blue Film and Device. Solar Energy Materials and Solar Cells, 177, 9-14. [Google Scholar] [CrossRef] 
[34] Wen, R., Granqvist, C.G. and Niklasson, G.A. (2015) Eliminating Degradation and Uncovering Ion-Trapping Dynamics in Electrochromic WO3 Thin Films. Nature Materials, 14, 996-1001. [Google Scholar] [CrossRef] [PubMed]
[35] Li, H., Jiang, X., Ye, W., Zhang, H., Zhou, L., Zhang, F., et al. (2019) Fully Photon Modulated Heterostructure for Neuromorphic Computing. Nano Energy, 65, Article ID: 104000. [Google Scholar] [CrossRef] 
[36] Yang, B., Dong, Y. and Chen, X. (2024) Feature-Enhanced Artificial Visual Perception Based on Superlinear Voltage-Reflectance Responses of Electrochromic Arrays. ACS Photonics, 11, 1909-1919. [Google Scholar] [CrossRef] 

为你推荐