PUFs Analysis for Unstable Signals Using Analog Circuit

Abstract

To date, physical unclonable functions (PUFs) have been extensively examined, and are used to distinguish
genuine and counterfeit products. Moreover, they are also attracting attention as one of the
methods to solve the security problems of Internet of Things (IoT) devices. However, most PUFs are
based on integrated circuit (IC) memory and use digital modulation for authentication. This study
proposes a new PUF that uses analog circuits and analog values for authentication. The advantage of
analog circuits is that they can handle analog values. Moreover, their characteristics do not change
when the surrounding environment is adjusted. Research on analog PUFs that evaluate stable signals
and DC voltages has been proposed to date. This study uses an astable multivibrator to analyze PUFs
for unstable signals. For analysis, we examine the conventional method of calculating the hamming
distance of digital values and the method using machine learning(ML). Consequently, we were able
to identify individuals with unsteady signals from analog values when using ML.