A quantum-based ultra-low area nano-architecture for morphological processes
Abstract
A new transistor-less field-coupled nanocomputing (FCN) method for ultra-scale “nanochip” integration is called quantum-dot cellular automata (QCA). Electrostatic repulsion between electrons and the electron tunneling process in quantum dots is employed in QCA to depict digital circuitry. QCA technology is able to achieve higher clock speed, lower occupied chip area, and more energy efficiency than traditional complementary metal-oxide semiconductor (CMOS) technology. Irreversible majority gates are commonly utilized as the main building blocks in the development of QCA circuits. In order to create highly energy-efficient QCA circuits, some research has recently presented digital image processing design strategies that use majority gates as the primary building block. Image processing, which encompasses picture analysis, enhancement, and correction, is used to extract information from images and improve their quality. Among the primary methods for processing images are picture enhancement, restoration, compression, object recognition, and machine vision applications. However, morphological procedures such as dilation and erosion constitute the fundamental component of image processing and are widely applied in practical applications. In this study, QCA-optimized nanostructures are presented for applications in mathematical morphology, serving as erosion and dilation operators. The completed circuit comprises 33 cells and exhibits a temporal delay of approximately 0.75 during each clock cycle. A comparison with the best equivalent nano-architecture demonstrates a significant improvement in cell performance, scalability, and latency.
