A novel modeling attack resistant PUF design based on non-linear voltage transfer characteristics

Physical Unclonable Function (PUF) circuits are used for chip authentication. PUF designs rely on manufacturing process variations to produce unique response to input challenges. It has been shown that many PUF designs are vulnerable to machine learning (ML) attacks, where a model can be built to predict PUF response to any input after only a few observations. In this work, we propose a ML attack resistant PUF design based on a circuit block to implement a non-linear voltage transfer function. The proposed circuit is simple, exhibits high uniqueness and randomness. Further improvements are proposed to enhance PUF reliability. The proposed circuit was simulated in a 45nm technology process and the results indicate a significant improvement in ML attack resistance in comparison to traditional PUFs. Results on uniqueness and reliability are also presented.