With rapid development of emerging applications, especially the artificial intelligence and Internet of Things (IoT), a large amount of digital information needs to be replicated, stored, processed, and communicated. This in turn requires secure communication and/or authentication; for this, cryptographic keys are required. [1,2] Software-based data encryption methods based on pseudorandom number generators are no longer sufficient due to their vulnerability; encryption effectiveness is also affected by the key quality. [3] These concerns essentially force the future data-intensive applications to have builtin-hardware-based information security systems, rather than relying solely on the software for battling security attacks. However, traditional hardware encryption solutions often work by storing the keys used by different encryption algorithms in a nonvolatile memory array; the stored cryptographic keys are vulnerable to physical and side-channel attacks, such as by observing the power consumed or radiation emitted. [4] Therefore, an epochmaking hardware-security technology is urgently needed for the future technologies.Physical unclonable functions (PUFs), also known as the physical one-way functions, [5] are promising hardware security primitives that allow random, unique cryptographic keys to be extracted for different application scenarios, rather than being stored in nonvolatile memory arrays. PUFs utilize intrinsic manufacturing defects in the physical microstructure of hardware components to interact with different external stimuli (light, voltage, and magnetic field), to produce unclonable cryptographic primitives comparable to biometrics for hardware security authorization. [6] The first reported PUFs exploited specific 2D patterns generated by a nonuniform 3D structure being optically hashed. [5] In general, optical PUF requires bulky external equipment and is incompatible with the existing silicon fabrication processes, which hinders its further application. [7] Additionally, conventional silicon PUFs suffer from issues such as low entropy, susceptibility to noise interference, and inability to resist machine-learning (ML) attacks. [8,9] In recent years, several PUFs constructed using novel materials, such Printed electronics promises to drive the future data-intensive technologies, with its potential to fabricate novel devices over a large area with low cost on nontraditional substrates. In these emerging technologies, there exists a large digital information flow, which requires secure communication and authentication. Physical unclonable functions (PUFs) offer a promising builtin hardware-security system comparable to biometrical data, which can be constructed by device-specific intrinsic variations in the additive manufacturing process of active devices. However, printed PUFs typically exploit the inherent variation in layer thickness and roughness of active devices. The current in devices with enough significant changes to increase the robustness to external environment noise is still a challen...

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It is anticipated that the rapid development of the Internet of Things (IoT) will improve the quality of human life. Nonetheless, large amounts of data need to be replicated, stored, processed, and shared, posing formidable challenges to communication bandwidth and information security. Herein, it is reported that polyimide (PI) threshold‐switching memristors exhibit Gaussian conductance and randomly set voltage distribution with nonideal properties to create a compression and encryption engine with a single chip. The Gaussian conductance distribution is used to achieve compressed sensing (CS) to integrate encryption into compression, and the spontaneous formation of the one‐time‐sampling measurement matrix satisfies absolute security. Moreover, the bitstreams generated by randomly distributed set voltages are used to diffuse the ciphertext from CS to improve security. The engine is shown to be secure even if the eavesdropper knows both the plaintext and the corresponding ciphertext. It has compression performance advantages that take both efficiency and security into account. In addition, due to the superior high temperature and mechanical properties of PI, the engine can continue to function normally in harsh environments. Herein, an excellent solution is offered for ensuring the efficiency and security of IoT.

With the rapid development of emerging artificial intelligence technology, brain–computer interfaces are gradually moving from science fiction to reality, which has broad application prospects in the field of intelligent robots. Looking for devices that can connect and communicate with living biological tissues is expected to realize brain–computer interfaces and biological integration interfaces. Brain‐like neuromorphic devices based on memristors may have profound implications for bridging electronic neuromorphic and biological nervous systems. Ultra‐low working voltage is required if memristors are to be connected directly to biological nerve signals. Therefore, inspired by the high‐efficient computing and low power consumption of biological brain, memristors directly driven by the electrical signaling requirements of biological systems (bio‐voltage) are not only meaningful for low power neuromorphic computing but also very suitable to facilitate the integrated interactions with living biological cells. Herein, attention is focused on a detailed analysis of a rich variety of physical mechanisms underlying the various switching behaviors of bio‐voltage memristors. Next, the development of bio‐voltage memristors, from simulating artificial synaptic and neuronal functions to broad application prospects based on neuromorphic computing and bio‐electronic interfaces, is further reviewed. Furthermore, the challenges and the outlook of bio‐voltage memristors over the research field are discussed.

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