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Fundamentals of Nanoelectronics 1 ECE 4140/6140 • Instructor: Avik Ghosh ([email protected]) E315 Thornton (434-243-2347) • Class: MWF 11-11:50 (THND222) • HWs due: F beginning of class • Office hrs/Tutorial: Thursday evenings • Grader: Dincer Unluer ([email protected]) Course 2 General Information on Matlab http://its.virginia.edu/research/matlab/ 1) For students who want to install a copy on their personal computers (need to use the UVAnywhere to start-up Matlab) http://its.virginia.edu/research/matlab/download.html#student 2) For students that want to use the Hive (Virtual Computers with Matlab installed that you can remote login using your personal computer). http://its.virginia.edu/hive/ 3) Use the stack computers. 3 Course website http://people.virginia.edu/~ag7rq/4140-6140/13/courseweb.html 4 5 6 Text/References www.nanohub.org VIrginia NanOComputing (VINO) http://www.ece.virginia.edu/vino 7 Grading info Homeworks Wednesdays 25% 1st midterm ~Feb end 20% 2nd midterm ~Mar end 20% ~May start 35% Finals 8 Syllabus Ch 1 (Overview) Ch 2 (Schrodinger eqn) Ch 3 (SCF) 1st midterm (Feb end) Ch 4 (Bandstructure) Ch 5 (Subbands) 2nd midterm (Mar end) 3-4 lectures 4 lectures 2-3 lectures 2-3 lectures 2-3 lectures Ch 6 (Capacitance) 2 lectures Ch 7 (Level broadening) 3 lectures Ch 8(Coherent Transport) 3 lectures Ch 9 (Atom to Transistor) 2 lectures Final (May start) Ch 10 (Coulomb Blockade) 2 lectures 9 Ch1: The need for microscopic understanding of transport 10 The Device Researcher’s bread and butter Gate Source Drain Channel 11 The Device Researcher’s bread and butter Field effect transistor (FET) Source/Drain Contacts – very conductive Channel – limits resistance of FET Gate – controls resistance of FET 12 Why study transistors? Many chemical, biological and physical processes involve digital switching with various gates Channel Drain Source Insulating substrate M spin memories closed S O U R C E open ion channel switches (Mackinnon, Nature ’03) CHANNEL INSULATOR VG M S O U R C E D R A I N I D R A I N CHANNEL INSULATOR VG VD Molecular motors VD I Biosensors 13 What would we look at in an FET? Turn-on Gate Voltage S-D Current ID S-D Current ID Saturation Rise Source-Drain Voltage • What’s the physics behind these curves? (HW2) • What about current along z direction? 14 The driving force behind microelectronics • Moore’s Law: double # FETs/chip in 1.5 years How far can we scale transistors? New physics emerges at these lengthscales 16 Cramming more transistors onto a chip Shorter time for electron to move across channel More memory elements on a chip Cheaper Smaller, Faster, Cheaper From Ralph Cavin, NSF-Grantees’ Meeting, Dec 3 2008 Silicon transistors already at nanoscale ! Intel’s 2003 transistor 6 nm MOSFET Bruce Doris, IBM 0.7 nm thick MOSFET Uchida, IEDM 2003 Smallness quantum effects Quantum confinement Atomistic fluctuations Leakage, Tunneling Quantum Scattering 22 A major problem: Power dissipation! New physics needed – new kinds of computation (HW1) 23 Heat is a Burning problem! 24 How can we push technology forward? 25 Better Design Multiple Gates for superior field control (Intel’s Trigate/FinFET) 26 Better Materials? 27 The material ‘zoo’ !! 2 nm 5 nm S O U R C E Silicon Nanowires (Low m < 100 cm2/Vs) Organic Molecules ? (Reproducibility/ Gateability) D R A I N INSULATOR Source VG VD I CNTs (m ~ 10,000cm2/Vs) Hard to align into a circuit! < 10 nm Top Gate Drain Channel Bottom Gate Strained Si, SiGe (m ~ 270cm2/Vs) 15 nm Graphene Atomistic Models Drain Gate Concept Physics Nobel, 2010 Channel Source Architecture 29 New Principles? Metallic spintronics already exists! Harness electron’s spin “Spintronics” Multiferroics, Nanomagnetism GMR (Nobel, 2007) MRAMs 30 How can we model and design today’s devices? 31 Pushing the simulation envelope.. ~ 1023 atoms Drain Source Bulk Solid (“macro”) (Classical Drift-Diffusion) Channel Bottom Gate 80s ~ 106 atoms Clusters (“meso”) (Semiclassical Boltzmann Transport) Today ~ 10-100 atoms Molecules (“nano”) (Quantum Transport) Quantum corrections to classical concepts, usually experimentally motivated (e.g. Quantum Resistance, Quantum interference, etc) Problem: Cannot derive Quantum concepts from Classical equations !!! (e.g. Entanglement in quantum Computation) 32 Bottom-Up instead of Top-Down ~ 1023 atoms Drain Source Bulk Solid (“macro”) (Classical Drift-Diffusion) Channel Bottom Gate 80s ~ 106 atoms Clusters (“meso”) (Semiclassical Boltzmann Transport) Today ~ 10-100 atoms Molecules (“nano”) (Quantum Transport) Classical Concepts do come out of Quantum Mechanics Phase Breaking Events (“Decoherence”) 33 Bottom-Up instead of Top-Down L > 1 mm Bulk Solid (“macro”) (Classical Drift-Diffusion) L ~ 100s nm Clusters (“meso”) (Semiclassical Boltzmann Transport) Phase Breaking Events (“Decoherence”) L ~ 10 nm source Classical Concepts do come out of Quantum Mechanics drain Molecules (“nano”) (Quantum Transport) 34 The challenge: Quantum effects = Ohm’s Law NO MORE! R independent of material, geometry R = h/2q2 = 12.9 kW Fourier’s Law NO MORE! RQ independent of material, geometry k = p2kB2T/3h = 0.95pW/K R < R1 + R2 ! Fano Interference in QDs The challenge: Atomistic effects Nanotube Data Williams group, UVa Characterizing single molecular traps using noise patterns (UCLA) Back to familiar I-Vs Turn-on Gate Voltage S-D Current ID S-D Current ID Saturation Rise Source-Drain Voltage What does a device engineer look for? 37 Gate Dependence (Transfer Characteristics) S-D Current ID Subthreshold Swing (mV/decade) Vd=1 DIBL (mV/V) Turn-on Vd=0.5 Gate Voltage VT small to lower power dissipation S small to lower power dissipation (> 60 at room temperature) ON-OFF ratio high (bit error rate in logic) Low OFF current (static power dissipation) DIBL low (OFF current) 38 Drain Dependence Mobility S-D Current ID Output conductance Source-Drain Voltage Want high mobility (high ON current Faster switching) Want high output impedance (reliability) 39 How do we understand/model these IVs? Turn-on S-D Current ID S-D Current ID Saturation Gate Voltage Rise Source-Drain Voltage HW2 40 Starting point: Band-diagram Solids have energy bands (Ch 5) How do we locate the levels? 41 Filled levels Photoemission hn S + hn S+ + e- 42 Empty levels Inverse Photoemission hn S + e- S- + hn 43 Level separations Optical absorption hn S S* + hn 44 Filling up the Bands with Electrons E Empty levels Filled levels Metal (Copper) Insulator (Silica) (charges can’t move) (charges move) Semiconductor (Si) (charge movement 45 can be controlled)