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Graphene Nanostructures : Modeling, Simulation, and Applications in Electronics and Photonics PDF

225 Pages·2019·14.532 MB·English
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Graphene Nanostructures Graphene Nanostructures Modeling, Simulation, and Applications in Electronics and Photonics Yaser M. Banadaki Safura Sharifi Published by Jenny Stanford Publishing Pte. Ltd. Level 34, Centennial Tower 3 Temasek Avenue Singapore 039190 Email: [email protected] Web: www.jennystanford.com British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. Graphene Nanostructures: Modeling, Simulation, and Applications in Electronics and Photonics All rights reserved. This book, or parts thereof, may not be reproduced in any form Copyright © 2019 by Jenny Stanford Publishing Pte. Ltd. or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the publisher. For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher. ISBN 978-981-4800-36-5 (Hardcover) ISBN 978-0-429-02221-0 (eBook) Contents Preface 1. In troduc tion to Graphene i1x 1.1 Physical Geometry and Properties 1 2. 1G.r2a phenGer afoprh Ienntee gNraatneodr iCbirbcounit s 167 2.1 Introduction 17 2.2 Scaling Challenges of Silicon Electronics 18 2.3 Graphene-Based Field-Effect Transistors 20 3. 2Co.4m putGatriaopnhael nCea-rBriaesre Tdr aInnstepgorrat tMedo Cdeirlc oufi tGsN RFET 2227 3.1 Introduction 27 3.2 Quantum Transport Model 32 3.3 Quantum Capacitance in GNRFET 39 3.4 Computational Time 43 4. 3Sc.5a ling ESfufemcmts aorny Performance of GNRFETs 4449 4.1 Introduction 49 4.2 Device Structure 50 4.3 Transfer Characteristics of GNRFETs 51 4.4 Scaling Effects on Static Metric of GNRFETs 54 I I 4.4.1 OFF-Current 54 4.4.2 ON/ OFF Ratio 54 4.4.3 Subthreshold Swing 56 4.4.4 Drain-Induced Barrier Lowering 56 4.4.5 Voltage Transfer Characteristic 59 4.5 Scaling Effects on Switching Attributes of GNRFETs 62 4.5.1 Intrinsic Gate Capacitance 62 4.5.2 Intrinsic Cut-off Frequency 63 4.5.3 Intrinsic Gate-Delay Time 64 vi Contents 4.5.4 Power-Delay Product 66 5. 4W.6id th-DSeupmenmdaernyt Performance of GNRFETs 6771 5.1 Introduction 71 5.2 Device Structure 72 5.3 GNR Sub-bands 74 5.4 Width-Dependent Static Metrics of GNRFETs 77 I I 5.4.1 OFF-Current 81 5.4.2 ON/ OFF Ratio 83 5.4.3 Subthreshold Swing 83 5.5 Width-Dependent Switching Attribute of GNRFETs 85 5.5.1 Threshold Voltage 85 5.5.2 Transconductance 87 5.5.3 Intrinsic Gate Capacitance 88 5.5.4 Intrinsic Cut-off Frequency 89 5.5.5 Intrinsic Gate-Delay Time 91 6. 5A. 6SP ICE SPuhmysmicas-rBya sed Circuit Model of GNRFETs 9915 6.1 Introduction 95 6.2 GNRFET Structure 97 6.3 GNRFET Model 98 6.3.1 Computing GNR Sub-bands 99 6.3.2 Finding Channel Surface Potential 100 6.3.2.1 Computing channel charge 101 6.3.2.2 Computing transient capacitance charge 102 6.3.3 Current Modeling 103 6.3.3.1 Computing thermionic current 104 6.3.3.2 BTBT current and charge 104 6.3.4 Non-ballistic Transport 106 6.3.5 Extracting Fitting Parameters 110 6.4 Model Validation 112 6.4.1 Comparing with Computational NEGF Formalism 112 6.4.2 Comparing with Many-Body Problem 114 Contents vii 6.5 Effect of Edge Roughness on Device Characteristic 117 6.5.1 Transfer Characteristics of GNRFETs 117 6.5.2 OFF-State Characteristics of GNRFETs 118 7. 6G.r6a phenSeu-mBamseadry C ircuit Design 111295 7.1 Introduction 125 7.2 All-Graphene Circuits 126 7.3 Graphene Inverter 127 7.4 Power and Delay of GNRFET Circuits 130 7.5 GNRFET-Based Energy Recovery Logic Design 135 8. 7G.r6a phenSeu mSemnsairnyg and Energy Recovery 114427 8.1 Introduction 147 8.2 GNRFET-Based Temperature Sensors 148 8.3 GNRFET for Energy Harvesting 153 8.3.1 Thermoelectric Model 154 8.3.2 Electrical Conductivity 156 8.3.3 Seebeck Coefficient 157 8.3.4 Electrical Thermal Conductivity 158 ZT 8.3.5 Power Factor 158 8.3.6 Thermoelectric Figure-of-Merit 160 9. 8G.r4a phenSeu mPhmoatorny ic Properties and Applications 116615 9.1 Introduction 165 9.2 Photonic Properties 167 9.3 Graphene Photonic Applications 168 9.3.1 Transparent Conductive Films and Passive Photonic Devices 169 9.3.2 Photodetectors 170 9.3.3 Optical Modulator 171 9.3.4 Mode-Locked Laser 171 9.3.5 THz Wave Generator 172 9.3.6 Optical Polarization Controller 172 9.4 Optical Conductivity Model of Graphene 173 9.5 Summary 178 viii Contents 10. Graphene-Based Thermal Emitter 185 10.1 Introduction 185 10.2 Thermal Emitter and Blackbody Radiation 186 10.3 Tunable Narrowband Thermal Emitters 188 10.4 Graphene-Based Aperiodic Multilayer Structure 190 10.5 Selectivity, Tunability, and Switchability 192 Index10.6 Summary 197 205 Preface ix Preface Over the past few decades, there have been tremendous innovations in electronics and photonics. Integrated circuits are everywhere and an indispensable part of our life, ranging from portable electronics to telecommunications and transportation. It is estimated that more than 1 trillion semiconductor devices shipped for the first time in 2016 with a rising demand due to new application areas such as the Internet of things, wearable electronics, and robotics. According to the Semiconductor Industry Association, the semiconductor industry is responsible for the direct employment of roughly a quarter of a million and indirectly supports another 1 million jobs only in the United States. The development of ultra-fast-growing technologies mostly relies on fundamental understanding of novel materials with unique properties as well as new designs of device architectures with more diverse and better functionalities. In this regard, the promising approach for next-generation nanoscale electronics and photonics is to exploit the extraordinary characteristics of novel nanomaterials. The discovery of graphene, one atomic layer of carbon sheet, in 2004 has prompted research into its potential in developing future electronic and photonic devices owing to its exceptional electronic, photonic, and mechanical properties. The driving engine for the exponential growth of digital information processing systems is scaling down the transistor dimensions. For decades, this has enhanced the device performance and density. However, the International Technology Roadmap for Semiconductors (ITRS) states the end of Moore’s law in the next decade due to the scaling challenges of silicon-based complementary metal-oxide semiconductor (CMOS) electronics, e.g., extremely high power density. A large group of emerging materials and devices is being extensively studied to replace silicon due to its scaling limit in sight. Germanium has been substituted by silicon roughly half a century ago by moving up on group IV of the periodic table. Interestingly, moving up one more block, we reach carbon,

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