Engineering-Oriented Design of Drift-Resilient MTJ Random Number Generator via Hybrid Control Strategies
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912404031406080 |
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| author | Zhang, Ran Wan, Caihua Xu, Yingqian Li, Xiaohan Hoffmann, Raik Hindenberg, Meike Liu, Shiqiang Kong, Dehao Xiong, Shilong He, Shikun Vardar, Alptekin Dai, Qiang Gong, Junlu Sun, Yihui Zheng, Zejie Kämpfe, Thomas Yu, Guoqiang Han, Xiufeng |
| author_facet | Zhang, Ran Wan, Caihua Xu, Yingqian Li, Xiaohan Hoffmann, Raik Hindenberg, Meike Liu, Shiqiang Kong, Dehao Xiong, Shilong He, Shikun Vardar, Alptekin Dai, Qiang Gong, Junlu Sun, Yihui Zheng, Zejie Kämpfe, Thomas Yu, Guoqiang Han, Xiufeng |
| contents | Magnetic Tunnel Junctions (MTJs) have shown great promise as hardware sources for true random number generation (TRNG) due to their intrinsic stochastic switching behavior. However, practical deployment remains challenged by drift in switching probability caused by thermal fluctuations, device aging, and environmental instability. This work presents an engineering-oriented, drift-resilient MTJ-based TRNG architecture, enabled by a hybrid control strategy that combines self-stabilizing feedback with pulse width modulation. A key component is the Downcalibration-2 scheme, which updates the control parameter every two steps using only integer-resolution timing, ensuring excellent statistical quality without requiring bit discarding, pre-characterization, or external calibration. Extensive experimental measurements and numerical simulations demonstrate that this approach maintains stable randomness under dynamic temperature drift, using only simple digital logic. The proposed architecture offers high throughput, robustness, and scalability, making it well-suited for secure hardware applications, embedded systems, and edge computing environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Engineering-Oriented Design of Drift-Resilient MTJ Random Number Generator via Hybrid Control Strategies Zhang, Ran Wan, Caihua Xu, Yingqian Li, Xiaohan Hoffmann, Raik Hindenberg, Meike Liu, Shiqiang Kong, Dehao Xiong, Shilong He, Shikun Vardar, Alptekin Dai, Qiang Gong, Junlu Sun, Yihui Zheng, Zejie Kämpfe, Thomas Yu, Guoqiang Han, Xiufeng Applied Physics Disordered Systems and Neural Networks Systems and Control Magnetic Tunnel Junctions (MTJs) have shown great promise as hardware sources for true random number generation (TRNG) due to their intrinsic stochastic switching behavior. However, practical deployment remains challenged by drift in switching probability caused by thermal fluctuations, device aging, and environmental instability. This work presents an engineering-oriented, drift-resilient MTJ-based TRNG architecture, enabled by a hybrid control strategy that combines self-stabilizing feedback with pulse width modulation. A key component is the Downcalibration-2 scheme, which updates the control parameter every two steps using only integer-resolution timing, ensuring excellent statistical quality without requiring bit discarding, pre-characterization, or external calibration. Extensive experimental measurements and numerical simulations demonstrate that this approach maintains stable randomness under dynamic temperature drift, using only simple digital logic. The proposed architecture offers high throughput, robustness, and scalability, making it well-suited for secure hardware applications, embedded systems, and edge computing environments. |
| title | Engineering-Oriented Design of Drift-Resilient MTJ Random Number Generator via Hybrid Control Strategies |
| topic | Applied Physics Disordered Systems and Neural Networks Systems and Control |
| url | https://arxiv.org/abs/2501.15206 |