Natural Computing Paradigms for Predictable Nanoelectronics

Natural computing is a computational version of natural phenomena. This review addresses natural computing principles for performing Boolean functions in nanostructures. The taxonomy we propose to use in our analysis is technology-independent computing units called logic primitives. The logic primitives are intermediate data structures for encoding natural phenomena. The intermediate structure reflects the property of a phenomenon, while linking it to the computational properties of Boolean functions. In this paper, we consider logic primitives for various nanotechnologies, and their interpretation using the quantum dot, complementary biomolecular, DNA, neuromorphic, and collision phenomena. Natural phenomena are often considered with respect to the computational paradigm based on self-assembly. Self-assembly of nanostructures has the potential to emerge as a low cost alternative to top-down manufacturing. Molecular self-assemblies are characterized by the topological properties such as symmetry, similarity, and periodicity, which makes it indispensable in encoding the self-assembling structures in terms of Boolean function computing. We show that various forms of Boolean functions can be encoded using a particular class of fractals, namely, triangle-like fractals, thus exploiting the relationships between these fractals and data structures for Boolean function representation.