Sequential Circuit Design in Quantum-Dot Cellular Automata

In this work we present a novel probabilistic modeling scheme for sequential circuit design in quantum-dot cellular automata(QCA) technology. Clocked QCA circuits possess an inherent direction for flow of information which can be effectively modeled using Bayesian networks (BN). In sequential circuit design this presents a problem due to the presence of feedback cycles since BN are direct acyclic graphs (DAG). The model presented in this work can be constructed from a logic design layout in QCA and is shown to be a dynamic Bayesian Network (DBN). DBN are very powerful in modeling higher order spatial and temporal correlations that are present in most of the sequential circuits. The attractive feature of this graphical probabilistic model is that that it not only makes the dependency relationships amongst node explicit, but it also serves as a computational mechanism for probabilistic inference. We analyze our work by modeling clocked QCA circuits for SR F/F, JK F/F and RAM designs.