This paper proposes the use of Cellular Non-Linear Networks (CNNs) as physical uncloneable functions (PUFs). We argue that analog circuits o®er higher security than existing digital PUFs and that the CNN paradigm allows us to build large, unclonable, and scalable analog PUFs, which still show a stable and repeatable input output behavior. CNNs are dynamical arrays of locally-interconected cells, with a cell dynamics that depends upon the interconnection strengths to their neighbors. They can be designed to evolve in time according to partial di®erential equations. If this equation describes a physical phenomenon, then the CNN can simulate a complex physical system on-chip. This can be exploited to create electrical PUFs with high relevant structural information content. To illustrate our paradigm at work, we design a circuit that directly emulates nonlinear wave propagation phenomena in a random media. It e®ectively translates the complexity of optical PUFs into electrical circuits.     «

This paper proposes the use of Cellular Non-Linear Networks (CNNs) as physical uncloneable functions (PUFs). We argue that analog circuits o®er higher security than existing digital PUFs and that the CNN paradigm allows us to build large, unclonable, and scalable analog PUFs, which still show a stable and repeatable input output behavior. CNNs are dynamical arrays of locally-interconected cells, with a cell dynamics that depends upon the interconnection strengths to their neighbors. They ca...     »