Information-Theoretic Modeling and Analysis of Stochastic Behaviors in Quantum-Dot Cellular Automata

Over the last two decades , quantum-dot cellular automata (QCA) has received significant attention as an emerging computing technology (Lent et al., 1993). The basic elements in this technology are the QCA cells, as shown in Fig. 1. Each cell contains two mobile electrons and four quantum dots located in the corners. As per the occupancy of the electrons in the dots, a QCA cell can take different states. A null state (polarization 0) occurs when the electrons are not settled. The other two states are referred to as polarization+1 and−1, denoted as P = +1 and P = −1 respectively. In these states due to Coulombic interactions, the electrons occupy the two diagonal configurations as shown in Fig. 1(b) and (c). These two states are identified with the so-called ground (stable) states. Any intermediate polarization between+1 and−1 is defined as a combination of states P = +1 and P = −1. By encoding the polarizations −1 and +1 into binary logic 0 and logic 1, QCA operation can be mapped into binary functions. For clocking and to allow QCA cells to reach a ground state, an adiabatic four-phased switching scheme has been introduced (Lent & Tougaw, 1997). By modulating the tunneling energy between the dots in a cell, this clocking scheme drives each cell through a depolarized state, a latching state, a hold phase, and then back to the depolarized state, such that the information flow is controlled through the QCA devices.