Photosensing PUF from an Intrinsically Random SnTe Memristor for Image Encryption and Recognition.
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| Title: | Photosensing PUF from an Intrinsically Random SnTe Memristor for Image Encryption and Recognition. |
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| Authors: | Xu, Wendi1 (AUTHOR), Zhang, Jia1 (AUTHOR), Xie, Junjie1 (AUTHOR), Xu, Tianzhu1 (AUTHOR), Wu, Jia1 (AUTHOR), Wang, Hong1 (AUTHOR) hongwang93@126.com |
| Source: | Nanomaterials (2079-4991). Jun2026, Vol. 16 Issue 12, p715. 12p. |
| Subjects: | Image encryption, Memristors, Computer security, Photosensitivity, Artificial synapses, Neuromorphics |
| Abstract: | Physical unclonable function (PUF) based on intrinsic device randomness has emerged as promising hardware security primitives, yet combining secure encryption with neuromorphic recognition within a single device platform remains challenging. Here, we demonstrate a photosensing PUF based on an intrinsically random SnTe memristor capable of both image encryption and memristive neural network recognition. The SnTe memristor, fabricated with an In2O3:SnO2/SnTe/Nb:SrTiO3 structure, exhibits stable resistive switching and stable retention exceeding 4000 s. Synaptic biomimetic behaviors including learning-experience emulation, short-term plasticity and long-term plasticity are also realized. Notably, the device displays pronounced optical sensitivity that produces stochastic photocurrent fluctuations originating from unavoidable device-to-device variations under illumination. By quantizing these random photocurrents, an encryption key stream is generated and utilized for image scrambling and diffusion. A memristive neural network is constructed to classify the encrypted images, achieving a recognition accuracy of 95.1% with a loss of 0.15 after 300 training epochs. This work establishes a viable pathway from intrinsic optical randomness to secure neuromorphic computing, highlighting the multifunctional potential of SnTe memristors in integrated hardware security and brain-inspired computation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Physical unclonable function (PUF) based on intrinsic device randomness has emerged as promising hardware security primitives, yet combining secure encryption with neuromorphic recognition within a single device platform remains challenging. Here, we demonstrate a photosensing PUF based on an intrinsically random SnTe memristor capable of both image encryption and memristive neural network recognition. The SnTe memristor, fabricated with an In2O3:SnO2/SnTe/Nb:SrTiO3 structure, exhibits stable resistive switching and stable retention exceeding 4000 s. Synaptic biomimetic behaviors including learning-experience emulation, short-term plasticity and long-term plasticity are also realized. Notably, the device displays pronounced optical sensitivity that produces stochastic photocurrent fluctuations originating from unavoidable device-to-device variations under illumination. By quantizing these random photocurrents, an encryption key stream is generated and utilized for image scrambling and diffusion. A memristive neural network is constructed to classify the encrypted images, achieving a recognition accuracy of 95.1% with a loss of 0.15 after 300 training epochs. This work establishes a viable pathway from intrinsic optical randomness to secure neuromorphic computing, highlighting the multifunctional potential of SnTe memristors in integrated hardware security and brain-inspired computation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20794991 |
| DOI: | 10.3390/nano16120715 |
