Abstract
There is an ever‐increasing demand for next‐generation devices that do not require passwords and are impervious to cloning. For traditional hardware security solutions in edge computing devices, inherent limitations are addressed by physical unclonable functions (PUF). However, realizing efficient roots of trust for resource constrained hardware remains extremely challenging, despite excellent demonstrations with conventional silicon circuits and archetypal oxide memristor‐based crossbars. An attractive, down‐scalable approach to design efficient cryptographic hardware is to harness memristive materials with a large‐degree‐of‐randomness in materials state variations, but this strategy is still not well understood. Here, the utilization of high‐degree‐of‐randomness amorphous (A) state variations associated with different operating conditions via thermal fluctuation effects is demonstrated, as well as an integrated framework for in memory computing and next generation security primitives, viz., APUF, for achieving secure key generation and device authentication. Near ideal uniformity and uniqueness without additional initial writing overheads in weak memristive A‐PUF is achieved. In‐memory computing empowers a strong exclusive OR (XOR‐) and‐repeat A PUF construction to avoid machine learning attacks, while rapid crystallization processes enable large‐sized‐key reconfigurability. These findings pave the way for achieving a broadly applicable security primitive for enhancing antipiracy of integrated systems and product authentication in supply chains. A strategy is developed for key generation and device authentication through the utilization of high‐degree‐of‐randomness amorphous (A) state variations concomitant with different operating conditions to address the challenge of realizing efficient roots of trust for resource constrained hardware. This approach enables excellent physical unclonable functions (PUF) performance in uniformity, uniqueness, writing overheads, and machine learning attack resilience.