36.3 A Modeling Attack Resilient Strong PUF with Feedback-SPN Structure Having <0.73% Bit Error Rate Through In-Cell Hot-Carrier Injection Burn-In

Author:

Liu Kunyang,Fu Zihan,Li Gen,Pu Hongliang,Guan Zhibo,Wang Xingyu,Chen Xinpeng,Shinohara Hirofumi

Publisher

IEEE

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine Learning Attacks on Challenge-Response Obfuscations in Strong PUFs;2024 IEEE International Symposium on Hardware Oriented Security and Trust (HOST);2024-05-06

2. SpongePUF: A Modeling Attack Resilient Strong PUF with Scalable Challenge Response Pair;2024 IEEE International Symposium on Hardware Oriented Security and Trust (HOST);2024-05-06

3. A New Reconfigurable True Random Number Generator and Physical Unclonable Function Unified Chip With On-Chip Auto-Calibration;IEEE Transactions on Circuits and Systems I: Regular Papers;2023-12

4. A 3.02 pJ/Bit 3T-APS-Based In-Sensor Strong PUF Featuring Near-100% Hardware Reuse Ratio and High Resilience to Machine Learning Attacks;IEEE Transactions on Circuits and Systems II: Express Briefs;2023-11

5. A 690fJ/Bit ML-Attack-Resilient Strong PUF Based on Subthreshold Voltage Attenuator Ring with Closed-Loop Feedback;ESSCIRC 2023- IEEE 49th European Solid State Circuits Conference (ESSCIRC);2023-09-11

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