A 3.02 pJ/Bit 3T-APS-Based In-Sensor Strong PUF Featuring Near-100% Hardware Reuse Ratio and High Resilience to Machine Learning Attacks

In this brief, we present an energy-efficient in-sensor strong physical unclonable function (PUF) based on the static entropy of 3-transistor active pixel sensor (3T-APS) structure that is widely-adopted in the CMOS image sensors (CIS). With ultra-small silicon area overhead of 0.25% dedicated to the added bias circuitries for column line and digital comparator for PUF bit generation, traditional 3T-APS-based CIS can be well-reused to construct the proposed strong PUF for authenticating the recorded images or videos. Taking advantage of the inherent high-resolution pixel array of CIS, the proposed strong PUF significantly extends the challenge-response-pair (CRP) space of the previous implementations. In addition, large CRP nonlinearity can be obtained by biasing the selected 3T-APS pixels at the subthreshold region, which further increases the complexity of the above extended CRP space and its resilience to various machine learning (ML) attacks. Moreover, the proposed strong PUF design is validated by the extensive measurement results of the prototype chips fabricated using a standard 65-nm CMOS process. Featuring a low energy consumption of 3.02 pJ/bit and a high area efficiency of $1.64\times 10^{33}$ bit/ $F^{2}$ , prediction error of 49.1%~51.84% can be achieved under various ML attacks based on artificial neural network, logistic regression, support vector machine and covariance matrix adaptation evolution strategy, with the number of adopted CRPs for training up to 10 million.