A machine learning resistant strong PUF using subthreshold voltage divider array in 65nm CMOS

Physically Unclonable Functions (PUFs) are extensively used in hardware security blocks as key-generators and light-weight authentication. With recent advances in machine learning (ML), most existing PUFs are shown to be vulnerable to modeling attacks based on ML algorithms. We present a novel silicon strong PUF architecture that cascades three strong PUFs to implement a single strong PUF that is resistant to ML based modeling attacks. Designed in 65nm CMOS technology, the proposed PUF with 2⁶⁰ challenge response pairs consume 0.43pJ/bit energy consumption from a power supply of 0.8V. The simulated inter-HD and intra-HD of the PUF are 0.5065 and 0.0696 respectively. When subjected to ML based modeling attacks, the prediction accuracy is 60% for logistic regression, artificial neural networking and support vector machine with nonlinear RBF kernel.