钛酸钙基忆阻器的多态信息传输在神经形态计算中的应用
Multi-State Information Transfer Based on Ca3Ti2O7 Memristor in the Application of Neuromorphic Computing
DOI: 10.12677/app.2026.165040, PDF,    国家自然科学基金支持
作者: 刘伟斌, 肖 阳, 唐新桂*：广东工业大学物理与光电工程学院，广东省传感物理与系统集成应用重点实验室，广东 广州
关键词: Ca3Ti2O7薄膜；人工突触；神经形态计算；Ca3Ti2O7 Film； Artificial Synapse； Neuromorphic Computing
摘要: 集成电路的工艺技术已进入发展瓶颈期，进一步实现高计算能力集成已成为一项重大挑战。在这项工作中，基于层状钙钛矿结构的Ca3Ti2O7的神经形态器件已被开发出来，而界面缺陷状态主导的载流子弛豫为实现具有突触控制能力的离子动力学提供了一条可靠的途径。测试结果表明，该结构具有与人工突触相似的电流增益/抑制特性，并且这种稳健的突触生长可控性可用于多比特信息处理的实现，相对传统计算架构可提供超高的单位信息处理集成度的能力。神经网络中卷积图像处理的96.8%的识别准确率体现了其计算潜力，有望超越冯·诺依曼系统的框架。
Abstract: The process technology of integrated circuits has entered a developmental plateau, and further advancement of high-computing power integration has become a major challenge. In this work, neuromorphic devices based on Ruddlesden-Popper (RP) structured Ca3Ti2O7 have been developed, and the interface defect state-dominated carrier relaxation provides a reliable pathway to achieve synaptically controllable ionic dynamics. Test results show that the structure has current gain/suppression characteristics similar to those of artificial synapses, and this robust synaptic growth controllability can be utilized for the realization of multi-bit information processing. Compared with traditional computing architectures, it offers the ability to achieve an extremely high unit information processing integration rate. The 96.8% recognition accuracy of convolutional image processing in neural networks exemplifies the computational potential that promises to exceed the framework of von Neumann systems.
文章引用：刘伟斌, 肖阳, 唐新桂. 钛酸钙基忆阻器的多态信息传输在神经形态计算中的应用[J]. 应用物理, 2026, 16(5): 439-446. https://doi.org/10.12677/app.2026.165040

参考文献

[1] Patel, M., Gosai, J., Lokhandwala, A. and Solanki, A. (2024) Slow Migration-Controlled Resistive Switching in Stable Dion-Jacobson Hybrid Perovskites for Flexible Memristive Applications. ACS Applied Electronic Materials, 6, 587-598. [Google Scholar] [CrossRef] 
[2] Wang, S., Song, L., Chen, W., Wang, G., Hao, E., Li, C., et al. (2023) Memristor-Based Intelligent Human-Like Neural Computing. Advanced Electronic Materials, 9, Article 2200877. [Google Scholar] [CrossRef] 
[3] 陈嘉颖. 氧化锌基类脑器件以及神经形态计算[D]: [博士学位论文]. 广州: 广东工业大学, 2025.
[4] Hu, H., Scholz, A., Singaraju, S.A., Tang, Y., Marques, G.C. and Aghassi-Hagmann, J. (2021) Inkjet-Printed Bipolar Resistive Switching Device Based on Ag/ZnO/Au Structure. Applied Physics Letters, 119, Article 112103. [Google Scholar] [CrossRef] 
[5] Lin, J., Wang, S. and Liu, H. (2021) Multi-Level Switching of Al-Doped HfO2 RRAM with a Single Voltage Amplitude Set Pulse. Electronics, 10, Article 731. [Google Scholar] [CrossRef] 
[6] Yang, D.P., Li, R.H., Tang, X.G., Li, D.L. and Sun, Q.J. (2025) Photoelectric Dual Mode Sensing System Based on One-Step Fabricated Heterojunction Artificial Synapses Device. Materials Science and Engineering: R: Reports, 165, Article 101021. [Google Scholar] [CrossRef] 
[7] Kim, M.K., Kim, I.J. and Lee, J.S. (2021) Oxide Semiconductor-Based Ferroelectric Thin-Film Transistors for Advanced Neuromorphic Computing. Applied Physics Letters, 118, Article 032902. [Google Scholar] [CrossRef] 
[8] Ganapathi, K., Yoon, Y. and Salahuddin, S. (2010) Analysis of Inas Vertical and Lateral Band-to-Band Tunneling Transistors: Leveraging Vertical Tunneling for Improved Performance. Applied Physics Letters, 97, Article 033504. [Google Scholar] [CrossRef] 
[9] Wisniewski, P. and Majkusiak, B. (2018) Modeling the Tunnel Field-Effect Transistor Based on Different Tunneling Path Approaches. IEEE Transactions on Electron Devices, 65, 2626-2631. [Google Scholar] [CrossRef] 
[10] Shi, Y., Wang, S.Y., Ma, S., Lei, Y.L., Liu, H.L., Chen, X.L., et al. (2020) Nanoscale Imaging of Ferroelectric Domain and Resistance Switching in Hybrid Improper Ferroelectric Ca3Ti2O7 Thin Films. Physics Letters A, 384, Article 126609. [Google Scholar] [CrossRef] 
[11] Elcombe, M.M., Kisi, E.H., Hawkins, K.D., White, T.J., Goodman, P. and Matheson, S. (1991) Structure Determinations for Ca3Ti2O7, Ca4Ti3O10, Ca3.6Sr0.4Ti3O10 and a refinement of Sr3Ti2O7. Acta Crystallographica Section B Structural Science, 47, 305-314. [Google Scholar] [CrossRef] 
[12] Li, X., Yang, L., Li, C.F., Liu, M.F., Fan, Z., Xie, Y.L., et al. (2017) Ultra-Low Coercive Field of Improper Ferroelectric Ca3Ti2O7 Epitaxial Thin Films. Applied Physics Letters, 110, Article 042901. [Google Scholar] [CrossRef] 
[13] Yoshiba, I., Iwai, T., Uehara, T. and Horikoshi, Y. (2007) Area Selective Epitaxy of GaAs with AlGaAs Native Oxide Mask by Molecular Beam Epitaxy. Journal of Crystal Growth, 301, 190-193. [Google Scholar] [CrossRef] 
[14] Zhang, Z., Yang, X., Liu, K. and Wang, R. (2022) Epitaxy of 2D Materials toward Single Crystals. Advanced Science, 9, Article 2105201. [Google Scholar] [CrossRef] [PubMed]
[15] Lao, J., Yan, M., Tian, B., Jiang, C., Luo, C., Xie, Z., et al. (2022) Ultralow-Power Machine Vision with Self-Powered Sensor Reservoir. Advanced Science, 9, Article 2106092. [Google Scholar] [CrossRef] [PubMed]
[16] Wang, Y., Lv, Z., Chen, J., Wang, Z., Zhou, Y., Zhou, L., et al. (2018) Photonic Synapses Based on Inorganic Perovskite Quantum Dots for Neuromorphic Computing. Advanced Materials, 30, Article 1802883. [Google Scholar] [CrossRef] [PubMed]
[17] Yang, Q., Huang, J., Chen, Q., Chen, C., Chen, H. and Guo, T. (2022) Synaptic Transistor with Tunable Synaptic Behavior Based on a Thermo-Denatured Polar Polymer Material. Journal of Materials Chemistry C, 10, 5534-5541. [Google Scholar] [CrossRef] 
[18] Liu, J., Gong, J., Wei, H., Li, Y., Wu, H., Jiang, C., et al. (2022) A Bioinspired Flexible Neuromuscular System Based Thermal-Annealing-Free Perovskite with Passivation. Nature Communications, 13, Article No. 7427. [Google Scholar] [CrossRef] [PubMed]
[19] Feng, D., Niu, Z., Yang, J., Xu, W., Liu, S., Mao, X., et al. (2021) Flexible Artificial Synapse with Relearning Function Based on Ion Gel-Graphene FET. Nano Energy, 90, Article 106526. [Google Scholar] [CrossRef] 
[20] Ravichandran, V., Li, C., Banagozar, A., Yang, J.J. and Xia, Q. (2018) Artificial Neural Networks Based on Memristive Devices. Science China Information Sciences, 61, Article 060423. [Google Scholar] [CrossRef] 
[21] Lee, S.T. and Lee, J.H. (2023) Neuromorphic Computing Using Random Synaptic Feedback Weights for Error Backpropagation in NAND Flash Memory-Based Synaptic Devices. IEEE Transactions on Electron Devices, 70, 1019-1024. [Google Scholar] [CrossRef] 
[22] Shen, C., Gao, X., Chen, C., Ren, S., Xu, J., Xia, Y., et al. (2022) ZnO Nanowire Optoelectronic Synapse for Neuromorphic Computing. Nanotechnology, 33, Article 065205. [Google Scholar] [CrossRef] [PubMed]
[23] Wang, L., Wang, X., Zhang, Y., Li, R., Ma, T., Leng, K., et al. (2020) Exploring Ferroelectric Switching in α-In2Se3 for Neuromorphic Computing. Advanced Functional Materials, 30, Article 2004609. [Google Scholar] [CrossRef] 

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