Neural network simulation and evolutionary synthesis of QCA circuits
CMOS technology miniaturization limits have promoted research on new alternatives which can keep the technologically advanced level of the last decades. Quantum-dot Cellular Automata (QCA) is a new technology in the nanometer scale that has been considered as one of these alternatives. QCA have a large potential in the development of circuits with high space density and low heat dissipation and allow the development of faster computers with lower power consumption. Differently from conventional technologies, QCA do not codify information by means of electric current flow, but rather by the configuration of electrical charges in the interior of the cells. The Coulomb interaction between cells is responsible for the flow of information. This paper proposes the use of computational intelligence techniques in the simulation and in the automatic synthesis of QCA circuits. The first results show that these techniques may play an important role in this research area since they are capable of simulating efficiently and fast, synthesizing optimized circuits with a reduced number of cells. Such optimization reduces the possibility of failures and guarantees higher speed.
quantum-dot cellular automata evolvable hardware genetic algorithm artificial neural networks nanotechnology nanoelectronics