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As data volume and complexity increase, conventional software‐based encryption methods face significant challenges, including vulnerability to attacks and high computational demands. This study proposes a hardware‐based cryptographic engine using a dual‐polarity memristive crossbar array with Ta/HfO2/RuO2 memristors, utilizing stochastic and deterministic operations for enhanced security and efficiency. The system integrates random key generation and XOR‐based logic operations within the memristive array, enabling robust encryption and decryption of binary data with a fully resistive data representation. Experimental validation of alphabet image data encryption and the array scale simulation of individual fingerprint authentication are conducted to validate the robustness of the proposed hardware and cryptographic method. The results demonstrate the potential of the proposed hardware for homomorphic encryption, enabling secure data processing without decryption, which can pave the way for memristive hardware to evolve into resistance‐based digital computing hardware. [ABSTRACT FROM AUTHOR]

This study introduces a Ta 2 O 5 -based self-rectifying memristor (SRM) with an Al 2 O 3 interfacial layer adopted to improve switching uniformity, read voltage margin, and long-term retention. The Pt/Ta 2 O 5 /Al 2 O 3 /TiN (PTAT) device exhibits a 10 5 rectification ratio, 10 4 on/off ratio, 2 × 10 6 endurance, and retention of 10 4 s at 150 °C. A 3-layer 4 × 4 vertical resistive random access memory structure exhibits uniform switching parameters. The coefficient of variation (CV) for device-to-device measurements is 0.23 for the low resistance state (LRS) and 0.22 for the high resistance state (HRS), while for cycle-to-cycle measurements, the CV is 0.38 for the LRS and 0.11 for the HRS. Finally, the present study demonstrates the superior performance of the PTAT devices in the context of hardware-aware training for a fully connected neural network implementation. These advancements position the PTAT device as a promising candidate for high-density three-dimensional storage class memory and low-power neural networks, offering the consistent performance and reliability necessary for future high-density storage applications.