Abstract
We propose the design of Physically Unclonable Functions (PUFs) exploiting the nonlinear behavior of Cellular Neural Networks (CNNs). Our work derives from some theoretical results achieved within the theory of CNNs, adapted to a simpler case. The theoretical analysis discussed in this work has a general validity, whereas the presented basic hardware solution (i.e., the PUF electronic implementation) has to be understood as a reference demonstrating circuit to be further optimized and developed for a profitable use of the proposed approach. Theoretical results have been validated experimentally.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Circuits and Systems I: Regular Papers
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
