Silicon physical unclonable functions (PUF) utilize fabrication variation to extract information that will be unique for each chip. However, fabrication variation has a very strong spatial correlation and thus the PUF information will not be statistically random, which causes security threats to silicon PUF. We propose to decouple the unwanted systematic variation from the desired random variation through a regression-based distiller. In our experiments, we show that information generated by existing PUF schemes fail to pass NIST randomness test. However, our proposed method can provide statistically random PUF information and thus bolster the security characteristics of existing PUF schemes.

The silicon physical unclonable functions (PUF) utilize the uncontrollable variations during integrated circuit (IC) fabrication process to facilitate security related applications such as IC authentication. In this paper, we describe a new framework to generate secure PUF secret from ring oscillator (RO) PUF with improved hardware efficiency. Our work is based on the recently proposed group-based RO PUF with the following novel concepts: an entropy distiller to filter the systematic variation; a simplified grouping algorithm to partition the ROs into groups; a new syndrome coding scheme to facilitate error correction; and an entropy packing method to enhance coding efficiency and security. Using RO PUF dataset available in the public domain, we demonstrate these concepts can create PUF secret that can pass the NIST randomness and stability tests. Compared to other state-of-the-art RO PUF design, our approach can generate an average of 72% more PUF secret with the same amount of hardware.