The quantum-dot large-neighborhood cellular nonlinear network (QLN-CNN) in nanotechnology

Abstract

In this paper, the quantum-dot Largeneighborhood cellular neural (nonlinear) network (QLN-CNN) is proposed and analyzed. In the proposed QLN-CNN, the quantum dots are used to realized neuron cells whereas the strength of Coulombic forces among neurons are used as weights among neurons. The proposed QLN-CNN can perform the functions of image noise removal. It has small chip area and high cell density. Moreover, the power dissipation is very low. Thus large-size QLN-CNN could be realized for nanoelectronic systems. research efforts have been devoted to the implementation of CNNs using the QCA [ 3 ] . However, the LN-CNN realization using the QCA has not yet proposed so far. In this paper, the quantum-dot LN-CNN (QLN-CNN) is proposed and analyzed. In the proposed QLN-CNN, the QCA are used to form CNN neuron cells [4]. The Coulombic forces among neurons are used to realize the synaptic weights among neurons. The function of noise removal processing has been successfully performed in the QLN-CNN. This verifies the correct function of the proposed QLN-CNN. It has small chip area and low power dissipation, being suitable for large array application.

Nanoelectronics and Nanospintronics: Fundamentals and Materials Perspective

“…New architectures have been investigated recently; these architectures take advantages of nearest neighbors or proximity interactions, and aim to minimize long distance interconnects and can incorporate fault tolerance conveniently to form new information processing paradigms. A revisit of architectures such as cellular automata, cellular nonlinear network, cross net molecular computing and eventually quantum computing in the context of using new nanodevices has recently emerged [3,[40][41][42][43][44][45][46][47]. Some of these architectures have been shown to outperform the conventional computer in some specific tasks.…”