Download Molecular Electronic Devices

Fundamentals of
Nanoelectronics
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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
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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.
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Course website
http://people.virginia.edu/~ag7rq/4140-6140/13/courseweb.html
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5
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Text/References
www.nanohub.org
VIrginia NanOComputing (VINO)
http://www.ece.virginia.edu/vino
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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
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Ch1: The need for microscopic
understanding of transport
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The Device Researcher’s bread and butter
Gate
Source
Drain
Channel
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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
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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
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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?
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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
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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
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A major problem: Power dissipation!
New physics needed – new kinds of computation
(HW1)
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Heat is a Burning problem!
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How can we push
technology forward?
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Better Design
Multiple Gates for superior field control
(Intel’s Trigate/FinFET)
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Better Materials?
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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
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New Principles?
Metallic spintronics
already exists!
Harness electron’s spin
“Spintronics”
Multiferroics, Nanomagnetism
GMR (Nobel, 2007)
MRAMs
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How can we model and
design today’s devices?
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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)
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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”)
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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)
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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?
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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)
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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)
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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
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Starting point: Band-diagram
Solids have energy bands (Ch 5)
How do we locate the levels?
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Filled levels  Photoemission
hn
S + hn  S+ + e-
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Empty levels  Inverse Photoemission
hn
S + e-  S- + hn
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Level separations  Optical absorption
hn
S  S* + hn
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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
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can be controlled)