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<p>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<sub>x</sub>/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<sup>8</sup> and a nonlinearity of over 10<sup>5</sup>, 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.</p>

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Self-rectifying memristors with high rectification ratio for attack-resilient autonomous driving systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Guobin Zhang,
  • Xuemeng Fan,
  • Jie Wang,
  • Zijian Wang,
  • Zhejia Zhang,
  • Pengtao Li,
  • Yitao Ma,
  • Kejie Huang,
  • Bin Yu,
  • Qing Wan,
  • Xiangshui Miao,
  • Yishu Zhang

摘要

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/HfOx/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 108 and a nonlinearity of over 105, 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.