Improving the Quality of FPGA RO-PUF by Principal Component Analysis (PCA)

AbstractRing Oscillator Physical Unclonable Functions (RO-PUFs) exploit the inherent manufacturing process variations, such as systematic and stochastic variations, to generate secret PUF responses that are unique to the device. Stochastic variations are random, while systematic variation exhibits a strong spatial correlation. Therefore, systematic process variation reduces the randomness of the PUF response. This lowers the ability of a PUF response to uniquely identify and authenticate individual devices. Further, the impact of systematic variation is paramount when the two ROs in comparison are placed far apart. Comparing the ROs that are close to each other does improve the randomness, but the responses generated are unreliable and limiting the possible Challenge-Response Pairs (CRPs). In this article, we are proposing a method to reduce the impact of systematic process variation on the RO oscillation frequencies by using Principal Component Analysis (PCA). Principal Components (PCs) model the directions of systematic and stochastic variation present on a device. By projecting the oscillation frequencies in the direction of stochastic variation, the impact of systematic variation can be reduced. Our proposed method neither restricts the placement of ROs to close groups nor limits the possible CRPs. The method is evaluated on a large population of 218 Xilinx Artix-7 FPGAs. To evaluate the efficiency of the proposed method, we purposely paired the ROs that are placed far apart on the FPGA fabric. Results obtained prove the ability of the proposed method in removing the impact of systematic variation on the oscillation frequencies and thereby producing truly random responses.