Computing with Multi-Value Logic in Quantum Dot Cellular Automata - Reza Sabbaghi-Nadooshan, Reza Akbari-Hasanjani, Leila Dehbozorgi, Majid Haghparast, Hamid Reza Akbari-Hasanjani

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Computing with Multi-Value Logic in Quantum Dot Cellular Automata (eBook)

Reza Sabbaghi-Nadooshan, Reza Akbari-Hasanjani, Leila Dehbozorgi, Majid Haghparast, Hamid Reza Akbari-Hasanjani (Autoren)

eBook Download: EPUB

2024
472 Seiten
Wiley-IEEE Press (Verlag)
978-1-394-25395-1 (ISBN)

Lese- und Medienproben

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Meet the latest challenges in quantum computing with this cutting-edge volume

Miniaturization is one of the major forms (and drivers) of innovation in electronics and computing. In recent years, the rapid reduction in the size of semiconductors and other key elements of digital technology has created major challenges, which new technologies are being continuously mobilized to meet. Quantum dot cellular automata (QCA) is a technology with huge potential to meet these challenges, particularly if multi-value computing is brought to bear.

Computing with Multi-Value Logic in Quantum Dot Cellular Automata introduces this groundbreaking area of technology and its major applications. Using MATLAB^® software and a novel multi-value logic simulator, the book demonstrates that multi-value circuits with a function that approximates fuzzy logic are within reach of modern engineering and design. Rigorous and clear, this book offers a crucial introduction to the processes of designing multi-value logic circuits with QCA technology.

Readers will also find:

The tools required to design fuzzy-quantum controllers with high processing speed
Detailed discussion of topics including basic gate function, the energy consumption of QCA multi-value cells, and much more
Extensive MATLAB^® data and other worked-through examples

Computing with Multi-Value Logic in Quantum Dot Cellular Automata is ideal for researchers and readers who are looking for an explanation of the basic concepts required to design multi-value circuits in this field.

Reza Sabbaghi-Nadooshan, PhD, is a Professor with the Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Reza Akbari-Hasanjani, PhD, is with the Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Leila Dehbozorgi is with the Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Majid Haghparast is affiliated with the Faculty of Information Technology at the University of Jyväskylä, Jyväskylä, Finland.

Hamid Reza Akbari-Hasanjani, PhD, is with the School of Chemistry, Damghan University, Damghan, Iran.

Meet the latest challenges in quantum computing with this cutting-edge volume Miniaturization is one of the major forms (and drivers) of innovation in electronics and computing. In recent years, the rapid reduction in the size of semiconductors and other key elements of digital technology has created major challenges, which new technologies are being continuously mobilized to meet. Quantum dot cellular automata (QCA) is a technology with huge potential to meet these challenges, particularly if multi-value computing is brought to bear. Computing with Multi-Value Logic in Quantum Dot Cellular Automata introduces this groundbreaking area of technology and its major applications. Using MATLAB software and a novel multi-value logic simulator, the book demonstrates that multi-value circuits with a function that approximates fuzzy logic are within reach of modern engineering and design. Rigorous and clear, this book offers a crucial introduction to the processes of designing multi-value logic circuits with QCA technology. Readers will also find: The tools required to design fuzzy-quantum controllers with high processing speedDetailed discussion of topics including basic gate function, the energy consumption of QCA multi-value cells, and much moreExtensive MATLAB data and other worked-through examples Computing with Multi-Value Logic in Quantum Dot Cellular Automata is ideal for researchers and readers who are looking for an explanation of the basic concepts required to design multi-value circuits in this field.

List of Figures

Figure 1.1 The role of energy levels in the reduction of semiconductor gaps with the increase in nanocrystal size.
Figure 1.2 Basic structure of an uncoated QD.
Figure 1.3 The bandgap alignments.
Figure 1.4 Schematic representation of the emission mechanism in QDs.
Figure 1.5 Photoelectrochemical property of quantum dots under light irradiation: (a) generation of anodic current in the presence of electron donor compound and (b) generation of cathodic current in the presence of electron acceptor compound.
Figure 2.1 Comparison of top-down and bottom-up methods.
Figure 3.1 Polarizations of bQCA cells.
Figure 3.2 TQCA cell.
Figure 3.3 States of electron placement in a stable TQCA cell.
Figure 3.4 Clock in QCA in four phases.
Figure 3.5 90° wire.
Figure 3.6 45° wire.
Figure 3.7 Overlapping or plate wiring structure.
Figure 3.8 Multilayer wiring structure.
Figure 3.9 Transmission wire with two-phase clock difference.
Figure 3.10 NOT gate using two bQCA cells diagonally.
Figure 3.11 NOT gate using two diagonal NOT gates.
Figure 3.12 Inverter gate with reduced area.
Figure 3.13 Majority gate with three inputs.
Figure 3.14 The majority gate with five inputs.
Figure 3.15 AND gate structure.
Figure 3.16 OR gate structure.
Figure 4.1 TQCA polarization simulation diagram.
Figure 4.2 Polarization diagram in QQCA based on calculations.
Figure 4.3 Polarization diagram in QuQCA.
Figure 5.1 Polarizations of the ternary model.
Figure 5.2 TQCA cell proposed in Ref. [129].
Figure 5.3 Distance between items of two proposed TQCA cells.
Figure 5.4 Distances between two ternary cells to calculate the external electrostatic energy.
Figure 5.5 Shows the proposed QQCA model.
Figure 5.6 Specifications of the used layers.
Figure 5.7 QQCA cell symbol.
Figure 5.8 Input drive block in QQCA.
Figure 5.9 The first proposed output drive.
Figure 5.10 Second structure of the input drive.
Figure 5.11 Distances between two ternary cells in the first layer.
Figure 5.12 Distances between two QQCA cells in the second layer.
Figure 5.13 Symbolic representation of two QCA cells.
Figure 5.14 Proposed QuQCA model.
Figure 5.15 Specifications of the ternary layer.
Figure 5.16 QuQCA cell symbol.
Figure 5.17 Schematic of the input drive using decoder.
Figure 5.18 Schematic of the first output drive.
Figure 5.19 Simplified output drive model [131].
Figure 5.20 Second input drive in QuQCA.
Figure 5.21 TQCA-based half-adder circuit.
Figure 5.22 Distances between electrons in the first layer.
Figure 5.23 Distances between electrons in the second layer.
Figure 5.24 Symbolic representation of two adjacent cells in QuQCA.
Figure 5.25 Block diagram of the studied fuzzy system.
Figure 5.26 Fuzzy system rule base simulation: (a) the circuit representation and (b) the simulation results of the rule base.
Figure 5.27 Quantum gates.
Figure 6.1 Effect of two neighboring TQCA cells at different kinetic energies.
Figure 6.2 Interactions of two cells in the ternary layer.
Figure 6.3 Interactions of two cells in the binary layer.
Figure 6.4 Interactions of two neighboring cells in the ternary layers of QuQCA cells.
Figure 6.5 Power consumption in TQCA as per polarization set {−2, 0, 2}.
Figure 6.6 Energy consumption in QQCA using quantum computation.
Figure 6.7 Power consumption in the proposed QQCA cell.
Figure 6.8 Power consumption using QuQCA quantum calculations.
Figure 6.9 Power consumption in the proposed QuQCA cell.
Figure 7.1 Ternary NOT gate.
Figure 7.2 Two adjacent TQCA cells (wire).
Figure 7.3 Majority ternary gate.
Figure 7.4 (a) AND gate structure with a fixed input in state A and (b) OR gate structure with a fixed input in state B.
Figure 7.5 QQCA-based NOT gate.
Figure 7.6 Two QQCA cells (QQCA-based wire).
Figure 7.7 QQCA majority gates: (a) standard majority gate and (b) diagonal majority gate.
Figure 7.8 AND and OR gates with two inputs and one fixed input (a) AND gate structure with one fixed input in state A and (b) OR gate structure with one fixed input in state D.
Figure 7.9 Representation of a NOT gate using the QuQCA-based symbolic model.
Figure 7.10 Two neighboring cells using the QuQCA symbolic model.
Figure 7.11 QuQCA majority gate.
Figure 7.12 QQCA-based AND and OR gate structures: (a) AND gate structure with a fixed input in state A and (b) OR gate structure with a fixed input in state E.
Figure 8.1 Environment of TQCASim software version 1.0.11.1.
Figure 8.2 TQCA cell options.
Figure 8.3 Simulation of a three-value majority gate: (a) majority gate designed with TQCASim, (b) majority gate waveform, and (c) the truth table obtained from the software in TQCA.
Figure 8.4 Simulation of ternary AND gates; (a) AND gates designed with TQCASim software, (b) AND gate waveform obtained by TQCASim, and (c) AND gate truth table obtained by TQCASim.
Figure 8.5 Simulation of the ternary OR gate: (a) OR gate designed by TQCASim, (b) OR gate waveform using TQCASim, and (c) OR gate truth table obtained from TQCASim.
Figure 8.6 Simulation of two TQCA cells: (a) the two cells simulated in TQCASim, (b) two-cell interaction waveform using TQCASim, and (c) the truth table of two cells simulated in TQCASim.
Figure 8.7 Simulation of a ternary wire with an odd number of cells: (a) odd number of cells in a wire simulated in TQCASim, (b) interaction waveform of the wire in TQCASim, and (c) truth table of the wire in TQCASim.
Figure 8.8 Simulation of a ternary wire with an even number of cells: (a) ternary wire with an even number of TQCA cells, (b) interaction waveform of the wire in TQCASim, and (c) truth table of the wire simulated in TQCASim.
Figure 8.9 Environment of QQCASim software version 1.0.0.1.
Figure 8.10 Specifications of QQCA cells.
Figure 8.11 Simulation of a quaternary majority gate: (a) majority gate designed with QQCASim, (b) the majority gate waveform, and (c) truth table obtained from the software.
Figure 8.12 Simulation of the quaternary AND gate: (a) AND gate designed using QQCASim, (b) AND gate waveform in QQCASim, and (c) AND gate truth table obtained by QQCASim.
Figure 8.13 Simulation of the quaternary OR gate: (a) OR gate designed in QQCASim, (b) OR gate waveform using QQCASim, and (c) OR gate truth table obtained using QQCASim.
Figure 8.14 Quaternary NOT gate simulation: (a) NOT gate in QQCASim, (b) NOT gate output waveform, and (c) NOT gate truth table simulation using QQCASim.
Figure 8.15 A quaternary quantum wire: (a) quantum wire, (b) quantum wire output waveform, and (c) quantum wire truth table, all obtained using QQCASim.
Figure 9.1 (a) Comparison of von Neumann and PIM structures, (b) representation of Akers array and a logic cell with three inputs X, Y, and Z and two identical outputs, and (c) stripping process in Akers array to obtain output.
Figure 9.2 Three different fault-tolerant three-input majority gates.
Figure 10.1 BPIM1: (a) implementation in binary QCA and (b) simulation results.
Figure 10.2 BPIM2: (a) implementation in binary QCA and (b) simulation results.
Figure 10.3 BPIM3: (a) block diagram of the proposed model, (b) implementation in binary QCA, and (c) simulation results.
Figure 10.4 BPIM4: (a) block diagram, (b) implementation in binary QCA, and (c) simulation results.
Figure 10.5 Cell numbering in BPIM1.
Figure 10.6 Cell numbering in BPIM2.
Figure 10.7 Cell numbering in BPIM3.
Figure 10.8 Cell numbering in BPIM4.
Figure 11.1 Block diagram of two-input AND gate using Akers array.
Figure 11.2 Two-input AND gate using PIM structure in binary QCA: (a) BAND1 and (b) BAND2.
Figure 11.3 AND gate simulation results using binary...

Erscheint lt. Verlag	6.8.2024
Sprache	englisch
Themenwelt	Technik ► Elektrotechnik / Energietechnik
Schlagworte	basic gate • basic memory structure • Hamiltonian matrix • Hartree-Fock polarization • logic Akers array • logic gate • majority gate • Multi-value logic • multi-value QCA • polarization • QuQCA cell • tenary QCA • ternary wire • TQCA simulator
ISBN-10	1-394-25395-8 / 1394253958
ISBN-13	978-1-394-25395-1 / 9781394253951

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