With the advancement of the Internet of Things (IoT), numerous electronic devices are connected to each other and exchange a vast amount of data via the Internet. As the number of connected devices increases, security concerns have become more significant. As one of the potential solutions for security issues, physical unclonable functions (PUFs) are emerging semiconductor devices that exploit inherent randomness generated during the manufacturing process and device operation. The unclonable security key generated from PUF can address the security issues of conventional electronic systems which depend solely on software. Although numerous PUFs based on the emerging memory devices requiring switching operations have been proposed, achieving hardware intrinsic PUF with low power consumption remains a key challenge.

The rapid advancement of medical AI has been hampered by a lack of available medical image datasets and labor-intensive labeling processes because it contains sensitive personal information. Generative networks can offer a solution by synthesizing diverse high-quality medical images using random numbers. However, software-based pseudo-random number generators (PRNGs) have limitations in the energy-efficient production of nonrepetitive random numbers because random numbers are generated by an algorithm using a deterministic initial value known as the PRGN seed. True random number generators (TRNGs) are crucial as noise input, but conventional TRNGs relying on thermal noise face stability and complexity issues. Therefore, when combining generative neural network-based AI technology with emerging device-based TRNG, it can contribute to developing medical AI models and novel medical systems.