Reconfigurable Physical Unclonable Function-Based Cryptographic Scheme Based on Memory-Enabled GFET Modulators

Modern society, increasingly connected by the Internet of Things (IoT), is generating and exchanging data at an unprecedented pace, including sensitive and classified information that is vulnerable to cyberattacks. To address this challenge, we propose a new hardware security primitive, the strong radio-frequency (RF) physically unclonable function (PUF), which leverages a graphene field-effect transistor (GFET)-based harmonic transponder. Thanks to graphene's exceptional electrical and mechanical properties, this PUF exhibits excellent randomness, uniqueness, and reliability for encryption applications. Additionally, the remarkable reconfigurability has been demonstrated by tuning the gate voltage, operating frequency, and different memory effect doping states, thereby strengthening reliability and robustness against various environmental factors. We further employ an artificial intelligence (AI)-assisted classification framework for device authentication from measured PUF responses, where a one-dimensional convolutional neural network (1D-CNN) encoder learns device-specific embeddings via contrastive (metric) learning with waveform augmentation, and query responses are identified using cosine-similarity nearest-neighbor matching, enabling robust recognition across multiple operating conditions. This proposed GFET PUF represents a new direction for hardware security and analog PUFs, with potential implications for various cryptographic and encryption applications in the era of IoT.