Nano-Electronic Devices www.com Dragica Vasileska • Stephen M. Goodnick Editors Nano-Electronic Devices Semiclassical and Quantum Transport Modeling ABC www.com Editors Dragica Vasileska Stephen M. Goodnick School of Electrical, Computer School of Electrical, Computer and Energy Engineering and Energy Engineering Arizona State University Arizona State University Tempe, Arizona Tempe, Arizona USA USA vasileska@asu.edu ISBN 978-1-4419-8839-3 e-ISBN 978-1-4419-8840-9 DOI 10.1007/978-1-4419-8840-9 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011928232 c Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis.
Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.com Preface Within this volume, we have attempted to present a comprehensive picture of the state of the art in transport modeling relevant for the simulation of nanoscale semiconductor devices. At the time of the publication of this book, advances in con- ventional planar semiconductor device scaling have resulted in production devices with gate lengths approaching 22 nanometers (at the time of writing this preface), while research devices with gate lengths of just a few nanometers have been demon- strated.
The semiconductor industry has been dominated by Si based Metal Oxide Semiconductor (MOS) transistors for over 40 years. However, at present, there is an increasing drive to integrate a diversity of materials such as III–V compound channel materials and high insulator dielectrics, and the introduction of radically new mate- rials such as graphene. At the same time, there have been extraordinary advances in new types of self-assembled materials such as carbon nanotubes, and semiconduc- tor nanowires, which offer the potential for new families of fully three-dimensional devices that will allow scaling to continue to atomic dimensions. As characteris- tic length scales decrease, the physics of transport changes dramatically.
For large dimensions compared to the mean free path for scattering (and the related phase coherence length), the semi-classical diffusive picture of charge transport holds, governed by the Boltzmann transport equation (BTE). On the other hand, for very short length scales, much less than the scattering mean free path, transport is coher- ent, and described in a purely quantum mechanical framework in terms of current associated with probability flux, usually from some idealized reservoir of carriers, i. The actual situation in current nanoscale devices is somewhere in be- tween these two pictures, which in the past has been referred to as a mesoscopic system (somewhere between microscopic and macroscopic). This regime perhaps the most interesting in terms of phenomena, but the most difficult to theoretically describe, in which both quantum mechanical phase coherent phenomena co-exist with phase randomizing, dissipative scattering processes, which requires a general theoretical approach capable of dealing with both on an equal footing.
In this book, we compile different approaches to the problem of transport in mesoscopic semi- conductor systems, ranging from semi-classical to fully quantum mechanical, in order to understand the advantages and limitations of each, as well as elucidating the complex and interesting phenomena encountered in ultra-small devices.com vi Preface In Chap. 1, we begin with an introduction to semi-classical device modeling, starting from the BTE, and deriving the associated moment equations leading to the widely used drift-diffusion and energy transport models, with different approaches for extraction of the transport parameters, and applications of this approach in some new novel energy conversion and sensing technologies. Chapter 2 considers the in- clusion of quantum mechanical effects such as tunneling and quantum confinement within the popular ensemble Monte Carlo (EMC) method for the solution of the semi-classical BTE, as well as the treatment of many body interactions between particles as well as between particles and impurities within a molecular dynamics framework. Chapter 3 introduces the full-band EMC method, in which the com- plete electronic bandstructure is used in the description of the electron and hole dynamics as well as scattering processes semi-classically.
A formalism based on the Pauli Master Equation is then introduced which allows for simulation of quan- tum transport within a similar framework to the BTE, and which is applied to some specific nanoscale structures where quantum effects are important such as resonant tunneling diodes (RTDs). Chapter 4 provides the general theoretical framework for quantum transport starting with the Liouiville-von Neumann equation, and then the various approximation schemes which lead to various forms of Master equations, including the Pauli and Boltzmann formalisms. Chapter 5 gives an overview of quantum transport based on the Wigner Function method, which utilizes a quantum mechanical distribution function in place of the semi-classical distribution function appearing in the BTE to obtain the Wigner–Boltzmann equation. Numerical ap- proaches for the solution of the Wigner–Boltzmann equation are discussed, and the application to quantum devices such as RTDs and nanoscale transistors presented.
Chapter 6 provides a description of quantum transport from a scattering matrix, wavefunction approach, based on the so-called Usuki method. Applications to trans- port through various prototype nanostructures such as quantum dots, nanowires and molecular systems are presented, including spin dependent phenomena which can be described within the same framework. The inclusion of scattering in real space within the Usuki method is then described, and its application to nanoscale MOS- FETs presented. Chapter 7 details an atomistic approach to transport appropriate for nanoscale systems, based on the empirical tight binding method for large systems of atoms such as quantum dots and nanoscale transistors.
We deeply acknowledge the valuable contributions that each of the authors made in writing these excellent chapters that this book consists of. Tempe Arizona, USA Dragica Vasileska 2011 Stephen M.com Contents 1 Classical Device Modeling. 1 Thomas Windbacher, Viktor Sverdlov, and Siegfried Selberherr 2 Quantum and Coulomb Effects in Nano Devices. 97 Dragica Vasileska, Hasanur Rahman Khan, Shaikh Shahid Ahmed, Gokula Kannan, and Christian Ringhofer 3 Semiclassical and Quantum Electronic Transport in Nanometer-Scale Structures: Empirical Pseudopotential Band Structure, Monte Carlo Simulations and Pauli Master Equation.
Fischetti, Bo Fu, Sudarshan Narayanan, and Jiseok Kim 4 Quantum Master Equations in Electronic Transport. Knezevic 5 Wigner Function Approach. Kosina 6 Simulating Transport in Nanodevices Using the Usuki Method. 359 Richard Akis, Matthew Gilbert, Gil Speyer, Aron Cummings, and David Ferry 7 Quantum Atomistic Simulations of Nanoelectronic Devices Using QuADS.
405 Shaikh Ahmed, Krishnakumari Yalavarthi, Vamsi Gaddipati, Abdussamad Muntahi, Sasi Sundaresan, Shareef Mohammed, Sharnali Islam, Ramya Hindupur, Ky Merrill, Dylan John, and Joshua Ogden vii www.com Contributors Shaik Shahid Ahamed Department of Electrical and Computer Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Drive, Carbondale, IL 62901, USA, ahmed@siu.edu Richard Akis Department of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA, richard.edu Aron Cummings Sandia National Laboratories, Livermore, CA, USA, aron. Dollfus Institute of Fundamental Electronics, CNRS, Univ. Paris-sud, Orsay, France, philippe.fr David Ferry Department of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA, dkferry@asu. Fischetti Department of Materials Science and Engineering, University of Texas at Dallas, 800 W., Richardson, TX 75080, USA, max.edu Bo Fu Department of Materials Science and Engineering, University of Texas at Dallas, 800 W., Richardson, TX 75080, USA, bo.edu Vamsi Gaddipathi Department of Electrical and Computer Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Drive, Carbondale, IL 62901, USA Matthew Gilbert Department of Electrical and Computer Engineering, University of Illinois, Urbana, IL, USA, matthewg@illinois.edu Ramya Hindupur Department of Electrical and Computer Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Drive, Carbondale, IL 62901, USA Sharnali Islam Department of Electrical and Computer Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Drive, Carbondale, IL 62901, USA Dylan John Department of Electrical and Computer Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Drive, Carbondale, IL 62901, USA Gokula Kannan Department of ECEE, Arizona State University, Tempe, AZ, USA, gokul@asu.edu ix www.com x Contributors Hasanur Rahman Khan Intel Corp., Hillsboro, OR, USA, hasanur.com Jiseok Kim Department of Electrical and Computer Engineering, University of Massachusetts, 100 Natural Resources Rd., Amherst, MA 01003, USA, jikim@ecs.edu Irena Knezevic University of Wisconsin-Madison, 3442 Engineering Hall, 1415 Engineering Drive, Madison, WI 53706-1691, USA, knezevic@engr.
Kosina Institute of Microelectronics, TU Vienna, Vienna, Austria, kosina@iue.at Shareef Mohammed Department of Electrical and Computer Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Drive, Carbondale, IL 62901, USA Abdussamad Muntahi Department of Electrical and Computer Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Drive, Carbondale, IL 62901, USA Sudarshan Narayanan Department of Materials Science and Engineering, University of Texas at Dallas, 800 W., Richardson, TX 75080, USA, sudarshan. Nedjalkov Institute of Microelectronics, TU Vienna, Vienna, Austria, mixi@iue.at Bozidar Novakovic University of Wisconsin-Madison, Madison, WI 53706, USA, novakovic@wisc.edu Joshua Ogden Department of Electrical and Computer Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Drive, Carbondale, IL 62901, USA D. Querlioz Institute of Fundamental Electronics, CNRS, Univ. Paris-sud, Orsay, France, damien.com Christian Ringhofer Department of Mathematics, Arizona State University, Tempe, AZ, USA, ringhofer@asu.edu Siegfried Selberherr Institute for Microelectronics, Gußhausstraße 27–29/E360, 1040 Vienna, Austria, Selberherr@iue.at Gil Speyer High Performance Computing Initiative, Arizona State University, Tempe, AZ, USA, speyer@asu.edu Sasi Sundaresan Department of Electrical and Computer Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Drive, Carbondale, IL 62901, USA Viktor Sverdlov Institute for Microelectronics, Gußhausstraße 27–29/E360, 1040 Vienna, Austria, Sverdlov@iue.at Dragica Vasileska School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA, vasileska@asu.com Contributors xi Thomas Windbacher Institute for Microelectronics, Gußhausstraße 27–29/E360, 1040 Vienna, Austria, Windbacher@iue.at Krishnakumari Yalavarthi Department of Electrical and Computer Engineering, Southern Illinois University at Carbondale, 1230 Lincoln Drive, Carbondale, IL 62901, USA www.com Chapter 1 Classical Device Modeling Thomas Windbacher, Viktor Sverdlov, and Siegfried Selberherr Abstract In this chapter an overview of classical device modeling will be given.
The first section is dedicated to the derivation of the Drift–Diffusion Transport model guided by physical reasoning. How to incorporate Fourier’s law to add a dependence on temperature gradients into the description, is presented. Quantum mechanical effects relevant for small devices are approximately covered by quantum correction models. After a discussion of the Boltzmann Transport equation and the systematic derivation of the Drift–Diffusion Transport model, the Hydrodynamic Transport model, the Energy Transport model, and the Six-Moments Transport model via a moments based method out of the Boltzmann Transport Equation, which is the essential topic of classical transport modeling, are highlighted.
The parame- ters required for the different transport models are addressed by an own section in conjunction with a comparison between the Six-Moments Transport model and the more rigorous Spherical Harmonics Expansion model, benchmarking the accuracy of the moments based approach. Some applications of classical transport models are presented, namely, analyses of solar cells, biologically sensitive field-effect transis- tors, and thermovoltaic elements. Each example is addressed with an introduction to the application and a description of its peculiarities. Keywords Classical device modeling · Drift–Diffusion · Six moments · Hydrody- namic transport · Energy transport · Solar cells · BioFET · Biologically sensitive field-effect transistor · Boltzmann transport · Thermoelectric · Figure of merit · Electrothermal transport · Spherical harmonics expansion 1 Heuristic Derivation of the Drift–Diffusion Transport Model Even though the method of moments, which will be presented in Sect.
5, is quite sophisticated and offers the possibility to extend a transport model to an arbitrary large and accurate set of equations, physically understanding of the model is not T. Windbacher () Institute for Microelectronics, Gußhausstraße 27–29/E360, 1040 Vienna, Austria e-mail: Windbacher@iue.), Nano-Electronic Devices: Semiclassical 1 and Quantum Transport Modeling, DOI 10.1007/978-1-4419-8840-9 1, c Springer Science+Business Media, LLC 2011 www. Windbacher et al. as instructive as a derivation via a heuristic approach.