Analog cellular neural network for application in physical unclonable functions

In this paper an analog cellular neural network is proposed with application in physical unclonable function design. Dynamical behavior of the circuit and its high sensitivity to the process variation can be exploited in a challenge-response security system. The proposed circuit can be used as unclonable core module in the secure systems for applications such as device identification/authentication and secret key generation. The proposed circuit is designed and simulated in 45-nm bulk CMOS technology. Monte Carlo simulation for this circuit, results in unpolarized Gaussian-shaped distribution for Hamming Distance between 4005 100-bit PUF instances.