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Updated: Sep 17, 2025

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具有高纠正比率的自我纠正记忆器用于抗攻击的自动驾驶系统
Guobin Zhang1,2, Xuemeng Fan1,2, Jie Wang1,2
1College of Integrated Circuits, Zhejiang University, Hangzhou, PR China.
Nature communications
|July 1, 2025
概括
这项研究介绍了一种自我纠正的memristor,用于安全的自动驾驶系统. 该设备显示出高性能和攻击弹性,增强智能运输的网络安全.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 智能设备,包括自动驾驶系统,由于大数据和物联网采用日益增加,容易受到网络攻击和数据泄露.
- 提高自动驾驶系统的安全性和可靠性对于智能运输的广泛采用至关重要.
研究的目的:
- 设计和制造一个自我校正的memristor,以提高自动驾驶系统的安全性.
- 评估基于memristor的交叉条数组的性能和攻击弹性,用于实时数据分类.
主要方法:
- 使用快速热制造的TiN/HfOx/Pt自纠正记忆器的制造.
- 设备性能的表征,包括纠正比率,非线性和设备变化.
- 实现用于人工神经网络执行和自动驾驶数据集分类的memristor交叉杆阵列.
主要成果:
- 自调整的memristor实现了调整比>108和非线性>105与低的设备变化 (3.32%的设备到设备,1.55%的循环到循环).
- 记忆器交叉阵列表现出强大的攻击弹性,在自动驾驶数据集上实现了84.25%的分类准确性.
- 即使在复杂的攻击场景下,性能也与软件模型 (84.34%) 相当.
结论:
- 自行纠正的memristors为提高自动驾驶系统的安全性和可靠性提供了一个有希望的解决方案.
- 基于memristor的交叉阵列可以执行硬件级人工神经网络计算,这对于智能汽车的实时数据处理至关重要.
- 这项研究提供了创新的策略,以保护未来的智能运输系统免受网络威胁.
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