Zhang Guobin, Fan Xuemeng, Wang Jie, Wang Zijian, Zhang Zhejia, Li Pengtao, Ma Yitao, Huang Kejie, Yu Bin, Wan Qing, Miao Xiangshui, Zhang Yishu
College of Integrated Circuits, Zhejiang University, Hangzhou, PR China.
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, PR China.
Nat Commun. 2025 Jul 1;16(1):5759. doi: 10.1038/s41467-025-60970-4.
With the rise of big data and the Internet of Things, smart devices, especially autonomous driving systems, have become prime targets for information leakage and cyberattacks. This study presents the design and fabrication of a self-rectifying memristor utilizing a TiN/HfO/Pt structure to enhance the security and reliability of autopilot systems. Following rapid thermal annealing treatment, the self-rectifying memristor demonstrates a recorded rectification ratio exceeding 10 and a nonlinearity of over 10, coupled with minimal device-to-device (3.32%) and cycle-to-cycle variations (1.55%). We further extend the application of self-rectifying memristors into crossbar arrays for the real-time classification of autonomous driving datasets, showcasing their capability to execute artificial neural networks at the hardware level. The proposed crossbar arrays exhibit robust attack resilience, achieving classification accuracy (84.25%) comparable to those of software models (84.34%), particularly under complex attack scenarios. This work not only highlights the potential of self-rectifying memristors in bolstering the security of autonomous driving systems but also offers innovative strategies for safeguarding future intelligent transportation systems.
随着大数据和物联网的兴起，智能设备，尤其是自动驾驶系统，已成为信息泄露和网络攻击的主要目标。本研究展示了一种利用TiN/HfO/Pt结构的自整流忆阻器的设计与制造，以提高自动驾驶系统的安全性和可靠性。经过快速热退火处理后，该自整流忆阻器的记录整流比超过10，非线性超过10，同时器件间变化最小（3.32%）且循环间变化最小（1.55%）。我们进一步将自整流忆阻器的应用扩展到交叉阵列，用于自动驾驶数据集的实时分类，展示了它们在硬件层面执行人工神经网络的能力。所提出的交叉阵列具有强大的抗攻击能力，在复杂攻击场景下，实现了与软件模型（84.34%）相当的分类准确率（84.25%）。这项工作不仅突出了自整流忆阻器在增强自动驾驶系统安全性方面的潜力，还为保障未来智能交通系统提供了创新策略。
