A Comparative Performance Analysis of Quantum Dot Cellular Automata Circuits using Key Performance Indicators

dc.contributor.authorKhan, Mohammad Ishtiaque
dc.contributor.authorRahat, Rafakat Uddin Kader
dc.contributor.authorHasan, Tanvir
dc.date.accessioned2022-06-14T09:46:00Z
dc.date.available2022-06-14T09:46:00Z
dc.date.issued2021-02
dc.descriptionMohammad Ishtiaque Khan (T161017) Md. Rafakat Uddin Kader Rahat (T161024) Md. Tanvir Hasan (T161026)en_US
dc.description.abstractQuantum Dot Cellular Automata (QCA) is the future candidate for circuit fabrication as replacement of CMOS technology. It has very attractive features such as the possibility of circuit placement in nano-scale, faster response i.e. more than THz and low power dissipation. In this thesis work, a comparative study of circuit has been done using Key performance indicators (KPI) for evaluation of circuit performance in QCA platform. For this, one-bit full adder and RAM (Random Access Memory) circuits are selected as samples. Though there is existence of QCA cost function for evaluation of circuit performance but some effective parameters are excluded from cost function equation. We have taken some key performance indicators into our work thorough proper study to evaluate circuit performance efficiently. The study says key performance indicators can affect the circuit performance effectively. Because the best circuit selection is more important during fabrication. Monetary expenditure is related. The internal relationship among key performance indicators and its behavior, effects on circuit performance are also investigated in this work. We concluded this work by proposing one solution from data investigation and modifying the QCA cost function.en_US
dc.identifier.citation64p.en_US
dc.identifier.urihttp://dspace.iiuc.ac.bd:8080/xmlui/handle/123456789/3313
dc.language.isoenen_US
dc.publisherDepartment of Electronic and Telecommunication Engineeringen_US
dc.titleA Comparative Performance Analysis of Quantum Dot Cellular Automata Circuits using Key Performance Indicatorsen_US
dc.typeThesisen_US

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