Chip-scale reconfigurable carbon nanotube physical unclonable functions

Abstract

Abstract With the rapid advancement of edge intelligence, ensuring the security of edge devices and protecting their communication has become critical. Physical unclonable functions, known as hardware fingerprints, are an emerging hardware security solution enabled with the physical variations inherent in the hardware systems. To facilitate a widespread edge deployment, here we present chip-scale reconfigurable physical unclonable functions built with carbon nanotube charge-trapping transistors, where the charge-trapping memory and physical variations of the transistors are harnessed to render over 1013 reconfigurable states and the demonstrated ideal physical unclonability. Arising from this, the physical unclonable functions prove robust resilience against advanced machine learning and artificial intelligence attacking (limiting success to ~50–60%) as well as brute force cracking (requesting an estimated 1016 years to crack). This performance, along with their scalability and low-power operation as well as cryogenic temperature robustness, position the physical unclonable functions a promising hardware security solution for edge intelligence. As a practical demonstration, we model self-driving vehicular network in Central Hong Kong and prove secure vehicle communication using the physical unclonable functions.