This document outlines the key aspects of using particle-based Monte Carlo simulations to solve the Boltzmann transport equation (BTE) for modeling semiconductor device transport. It describes how the BTE can be solved by decomposing carrier transport into free flight periods between scattering events. Random flight times are generated from the probability distribution of scattering rates. After each free flight, a scattering mechanism is chosen randomly based on its probability. New carrier momentum and energy are determined after each scattering event to model transport.
In this presentation the following topics are covered:
- Active debris removal techniques
- Tethered space tug
- Mathematical model
- Numerical simulation and analysis
- Results and conclusion
Presentation for the 5th Eucass - European Conference for Aerospace Sciences - Munich, Germany, 1-4 July 2013.
This poster was created in LaTeX on a Dell Inspiron laptop with a Linux Fedora Core 4 operating system. The background image and the animation snapshots are dxf meshes of elastic waveform solutions, rendered on a Windows machine using 3D Studio Max.
Describes the mathematics of the Calculus of Variations.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website on http://www.solohermelin.com
In this paper, we propose an improved quantum-behaved particle swarm optimization (QPSO), introducing chaos theory into QPSO and exerting logistic map to every particle position X(t) at a certain probability. In this improved QPSO, the logistic map is used to generate a set of chaotic offsets and produce multiple positions around X(t). According to their fitness, the particle's position is updated. In order to further enhance the diversity of particles, mutation operation is introduced into and acts on one dimension of the particle's position. What's more, the chaos and mutation probabilities are carefully selected. Through several typical function experiments, its result shows that the convergence accuracy of the improved QPSO is better than those of QPSO, so it is feasible and effective to introduce chaos theory and mutation operation into QPSO.
I am Paul G. I am a Mechanical Engineering Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. Matlab, University of Adelaide, Australia. I have been helping students with their homework for the past 10 years. I solve assignments related to Mechanical Engineering.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Mechanical Engineering Assignments.
REPORT SUMMARYVibration refers to a mechanical.docxdebishakespeare
REPORT SUMMARY
Vibration refers to a mechanical phenomenon involving oscillations about a point. These oscillations can be of any imaginable range of amplitudes and frequencies, with each combination having its own effect. These effects can be positive and purposefully induced, but they can also be unintentional and catastrophic. It's therefore imperative to understand how to classify and model vibration.
Within the classroom portion of ME 345, we discussed damped and undamped vibrations, appropriate models, and several of their properties. The purpose of Lab 3 is to give us the corresponding "hands-on" experience to cement our understanding of the theory.
As it turns out, vibration can be modeled with a simple spring-mass system (spring-mass-damper system for damped vibration). In order to create a mathematical model for our simple spring-mass system, we apply Newton's second law and sum the forces about the mass. After applying some of our knowledge of differential equations, the result is a second order linear differential equation (in vector form). This can easily be converted to the scalar version, from which it's easy to glean various properties of the vibration (i.e. natural frequency, period, etc.).
In the lab, we were provided with a PASCO motion sensor, USB link, ramp, and accompanying software. All of the aforementioned equipment was already assembled and connected. The ramp was set up at an angle with a stop on the elevated end and the motion sensor on the lower end. The sensor was connected to the USB link, which was in turn connected to the computer. We chose to use the Xplorer GLX software to interface with the sensor and record our data. After receiving our equipment, we gathered data on our spring's extension with a known load to derive a spring constant. We were provided with a small cart to which we attached weights to increase its mass. In order to model free vibration, we placed the cart on the track and attached it to the stop at the top of the ramp with a spring. After displacing the cart a certain distance from its equilibrium point, the cart was released and was allowed to oscillate on the track while we recorded its distance from the sensor. This was done with displacements of -20cm, -10cm, +10cm, and +20cm from the system's equilibrium point. After gathering this data for the "free" case, a magnet was attached to the front of the car, spaced as far from the track as possible. As the track is magnetic, this caused a slight damping effect, basically converting our spring-mass system to an underdamped spring-mass-damper system. After repeating the procedure for the "free" case, we moved the magnets as close to the track as possible (causing the system to become overdamped) and again repeated the procedure for the "free" case.
We were finally able to determine the period, phase angle, damping coefficients, and circular and cyclical frequencies for the three systems. There were similarities and differ ...
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
In this presentation the following topics are covered:
- Active debris removal techniques
- Tethered space tug
- Mathematical model
- Numerical simulation and analysis
- Results and conclusion
Presentation for the 5th Eucass - European Conference for Aerospace Sciences - Munich, Germany, 1-4 July 2013.
This poster was created in LaTeX on a Dell Inspiron laptop with a Linux Fedora Core 4 operating system. The background image and the animation snapshots are dxf meshes of elastic waveform solutions, rendered on a Windows machine using 3D Studio Max.
Describes the mathematics of the Calculus of Variations.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website on http://www.solohermelin.com
In this paper, we propose an improved quantum-behaved particle swarm optimization (QPSO), introducing chaos theory into QPSO and exerting logistic map to every particle position X(t) at a certain probability. In this improved QPSO, the logistic map is used to generate a set of chaotic offsets and produce multiple positions around X(t). According to their fitness, the particle's position is updated. In order to further enhance the diversity of particles, mutation operation is introduced into and acts on one dimension of the particle's position. What's more, the chaos and mutation probabilities are carefully selected. Through several typical function experiments, its result shows that the convergence accuracy of the improved QPSO is better than those of QPSO, so it is feasible and effective to introduce chaos theory and mutation operation into QPSO.
I am Paul G. I am a Mechanical Engineering Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. Matlab, University of Adelaide, Australia. I have been helping students with their homework for the past 10 years. I solve assignments related to Mechanical Engineering.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Mechanical Engineering Assignments.
REPORT SUMMARYVibration refers to a mechanical.docxdebishakespeare
REPORT SUMMARY
Vibration refers to a mechanical phenomenon involving oscillations about a point. These oscillations can be of any imaginable range of amplitudes and frequencies, with each combination having its own effect. These effects can be positive and purposefully induced, but they can also be unintentional and catastrophic. It's therefore imperative to understand how to classify and model vibration.
Within the classroom portion of ME 345, we discussed damped and undamped vibrations, appropriate models, and several of their properties. The purpose of Lab 3 is to give us the corresponding "hands-on" experience to cement our understanding of the theory.
As it turns out, vibration can be modeled with a simple spring-mass system (spring-mass-damper system for damped vibration). In order to create a mathematical model for our simple spring-mass system, we apply Newton's second law and sum the forces about the mass. After applying some of our knowledge of differential equations, the result is a second order linear differential equation (in vector form). This can easily be converted to the scalar version, from which it's easy to glean various properties of the vibration (i.e. natural frequency, period, etc.).
In the lab, we were provided with a PASCO motion sensor, USB link, ramp, and accompanying software. All of the aforementioned equipment was already assembled and connected. The ramp was set up at an angle with a stop on the elevated end and the motion sensor on the lower end. The sensor was connected to the USB link, which was in turn connected to the computer. We chose to use the Xplorer GLX software to interface with the sensor and record our data. After receiving our equipment, we gathered data on our spring's extension with a known load to derive a spring constant. We were provided with a small cart to which we attached weights to increase its mass. In order to model free vibration, we placed the cart on the track and attached it to the stop at the top of the ramp with a spring. After displacing the cart a certain distance from its equilibrium point, the cart was released and was allowed to oscillate on the track while we recorded its distance from the sensor. This was done with displacements of -20cm, -10cm, +10cm, and +20cm from the system's equilibrium point. After gathering this data for the "free" case, a magnet was attached to the front of the car, spaced as far from the track as possible. As the track is magnetic, this caused a slight damping effect, basically converting our spring-mass system to an underdamped spring-mass-damper system. After repeating the procedure for the "free" case, we moved the magnets as close to the track as possible (causing the system to become overdamped) and again repeated the procedure for the "free" case.
We were finally able to determine the period, phase angle, damping coefficients, and circular and cyclical frequencies for the three systems. There were similarities and differ ...
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
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June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Model Attribute Check Company Auto PropertyCeline George
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2. Outline:
What is Computational Electronics?
Semi-Classical Transport Theory
Drift-Diffusion Simulations
Hydrodynamic Simulations
Particle-Based Device Simulations
Inclusion of Tunneling and Size-Quantization Effects in Semi-Classical Simulators
Tunneling Effect: WKB Approximation and Transfer Matrix Approach
Quantum-Mechanical Size Quantization Effect
Drift-Diffusion and Hydrodynamics: Quantum Correction and Quantum
Moment Methods
Particle-Based Device Simulations: Effective Potential Approach
Quantum Transport
Direct Solution of the Schrodinger Equation (Usuki Method) and Theoretical
Basis of the Green’s Functions Approach (NEGF)
NEGF: Recursive Green’s Function Technique and CBR Approach
Atomistic Simulations – The Future
Prologue
3. Direct Solution of the Boltzmann
Transport Equation
Particle-Based Approaches
Spherical Harmonics
Numerical Solution of the Boltzmann-Poisson
Problem
In here we will focus on Particle-Based
(Monte Carlo) approaches to solving the
Boltzmann Transport Equation
4. Ways of Solving the BTE Using MCT
Single particle Monte Carlo Technique
Follow single particle for long enough time to
collect sufficient statistics
Practical for characterization of bulk materials
or inversion layers
Ensemble Monte Carlo Technique
MUST BE USED when modeling
SEMICONDUCTOR DEVICES to have the
complete self-consistency built in
Carlo Jacoboni and Lino Reggiani, The Monte Carlo method for the solution of charge
transport in semiconductors with applications to covalent materials, Rev. Mod. Phys. 55, 645
- 705 (1983).
5. Path-Integral Solution to the BTE
The path integral solution of the Boltzmann
Transport Equation (BTE), where L=Nt and
tn=nt, is of the form:
1
( )
0
( ) ( ') ( ', ( ) )
N
N m t
N m eff
m
f t t f p S p p eE N m t e
( ( ) )
m
g p eE N m t
K. K. Thornber and Richard P. Feynman,
Phys. Rev. B 1, 4099 (1970).
6. The two-step procedure is then found by
using N=1, which means that t=t, i.e.:
1 0
'
( ) ( ') ( ', ) t
eff
p
f t t f p S p p eE t e
0 ( )
g p eE t
Intermediate function that describes
the occupancy of the state (p+eEt)
at time t=0, which can be changed
due to scattering events (SCATTER)
Integration over a trajectory,
i.e.probability that no scattering
occurred within time integral t
(FREE FLIGHT)
+
7. Monte Carlo Approach to Solving the
Boltzmann Transport Equation
Using path integral formulation to the
BTE one can show that one can
decompose the solution procedure into
two components:
1. Carrier free-flights that are interrupted by
scattering events
2. Memory-less scattering events that change
the momentum and the energy of the particle
instantaneously
8. Particle Trajectories in Phase Space
Particle trajectories in k-space and real space
y
k
x
k
-e
x
x
x x
x x
x x
x
x
x
E
y
x
y
x
9. Carrier Free-Flights
The probability of an electron scattering in a small time interval dt is
(k)dt, where (k) is the total transition rate per unit time. Time
dependence originates from the change in k(t) during acceleration by
external forces
where v is the velocity of the particle.
The probability that an electron has not scattered after scattering at t = 0
is:
It is this (unnormalized) probability that we utilize as a non-uniform
distribution of free flight times over a semi-infinite interval 0 to . We
want to sample random flight times from this non-uniform distribution
using uniformly distributed random numbers over the interval 0 to 1.
t
t
t
d
n e
t
P 0
)
(
k
/
0 t
e
t B
v
E
k
k
10. Generation of Random Flight Times
Hence, we choose a random number
t
t
t
d
i e
r 0
1
,
k
Ith particle first random number
We have a problem with this integral!
We solve this by introducing a new, fictitious scattering
process which does not change energy or momentum:
)
(
)
(
)
(
)
( k
k
x
x
E
k S
ss
11. Generation of Random Flight Times
t
t
t
d
i e
r 0
1
,
k
i
i k
k )
(
)
( The sum runs over all the real
scattering processes. To this we
add the fictitious self-scattering
which is chosen to have a nice
property: 0
new
scatterers
real
i
ss k
k )
(
)
( 0
12. • The use of the full integral form of the free-flight probability
density function is tedious (unless k is invariant during the
free flight).
• The introduction of self-scattering (Rees, J. Phys. Chem.
Solids 30, 643, 1969) simplifies the procedure considerably.
• The properties of the self-scattering mechanism are that it
does not change either the energy or the momentum of the
particle.
• The self-scattering rate adjusts itself in time so that the total
scattering rate is constant. Under these circumstances, one
has that:
dt
e
dt
e
dt
t
P
t
t t
t
d
self
t
0
k
k
Self-Scattering
13. Self-Scattering
• Random flight times tr may be generated from P(t) above using
the direct method to get:
where r is a uniform random between 0 and 1 (and therefore r
and 1-r are the same).
• must be chosen (a priori) such that > (k(t)) during the entire
flight.
• Choosing a new tr after every collision generates a random walk
in k-space over which statistics concerning the occupancy of the
various states k are collected.
r
r
t
e
r r
tr
ln
ln
1
1
1
14. Free-Flight Scatter Sequence for
Ensemble Monte Carlo
= collisions
1
n
2
3
4
5
6
N
1
,
i
t
1
,
i
t
However, we need a
second time scale,
which provides the
times at which the
ensemble is
“stopped” and
averages are
computed.
Particle time
scale
15. Free-Flight
Scatter
Sequence
dte=dtau
dte ≥ t?
no yes
dt2 = dte dt2 = t
Call drift(dt2)
dte ≥ t?
yes
dte2 = dte
Call scatter_carrier()
Generate free-flight dt3
dtp=t-dte2
dt3 ≤ dtp?
no yes
dt2 = dtp dt2 = dt3
Call drift(dt2)
dte2=dte2+dt3
dte=dte2
no
yes
dte < t ?
dte=dte-t
dtau=dte
R. W. Hockney and J. W.
Eastwood, Computer Simulation
Using Particles, 1983.
16. Choice of Scattering Event Terminating
Free Flight
o At the end of the free flight ti, the type of scattering which ends the
flight (either real or self-scattering) must be chosen according to the
relative probabilities for each mechanism.
o Assume that the total scattering rate for each scattering mechanism
is a function only of the energy, E, of the particle at the end of the
free flight
where the rates due to the real scattering mechanisms are typically
stored in a lookup table.
o A histogram is formed of the scattering rates, and a random number
r is used as a pointer to select the right mechanism. This is
schematically shown on the next slide.
E
E
E
E pop
ac
i
self
17. Choice of Scattering Event Terminating
Free-Flight
We can make a table of the scattering processes at
the energy of the particle at the scattering time:
i
t
E
1
2
1
3
2
1
4
3
2
1
Self
5
4
3
2
1
0
1
3
2
4
5
Selection process
for scattering
0
r
19. Choice of the Final State After Scattering
Using a random number and probability
distribution function
Using analytical expressions (slides that follow)
0 0.5 1 1.5 2 2.5 3 3.5
0
1
2
3
4
5
6
x 10
4
Polar angle
Arbitrary
Units
20. 1. Isotropic scattering processes
cos 1 2 , 2
r r
2. Anisotropic scattering processes (Coulomb, POP)
kx
ky
kz
k
0
0
Step 1:
Determine 0 and 0
kx’
ky’
kz’
k
Step 2:
Assume
rotated
coordinate
system
Step 3:
kx’
ky’
kz’
k
k’
k’≠k for
inelastic
21. kx’
ky’
Step 3:
perform scattering
0
2
0
2
1 1 2
cos ,
r
k k
k k
E E
E E
POP
2 2
2
cos 1
1 4 (1 )
D
r
k L r
Coulomb
=2r for both
kx’
ky’
Step 4:
kxp = k’sin cos, kyp = k’sin*sin, kzp = k’cos
Return back to the original coordinate system:
kx = kxpcos0cos0-kypsin0+kzpcos0sin0
ky = kxpsin0cos0+kypcos0+kzpsin0sin0
kz = -kxpsin0+kzpcos0
22. Representative Simulation Results From
Bulk Simulations - GaAs
k-vector
-valley
X-valley [100]
L-valley [111]
Conduction bands
Valence bands
-valley table
-Mechanism1
-Mechanism2
- …
-MechanismN
L-valley table
-Mechanism1
-Mechanism2
- …
-MechanismNL
X-valley table
-Mechanism1
-Mechanism2
- …
-MechanismNx
Define scattering mechanisms for each valley
Call specified
scattering mechanisms subroutines
Renormalize scattering tables
Simulation Results Obtained by
D. Vasileska’s Monte Carlo Code.
23. parameters initialization
readin()
scattering table construction
sc_table()
histograms calculation
histograms()
Free-Flight-Scatter
free_flight_scatter()
histograms calculation
histograms()
write data
write()
?
carriers initialization
init()
t t t
Time t exceeds maximum
simulation time tmax
yes
no
Optional
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Energy [eV]
Cumulative
rate
[1/s]
-6 -4 -2 0 2 4 6
x 10
8
0
5
10
15
20
25
30
35
40
Wavevector ky [1/m]
Arbitrary
Units
Initial Distribution of the
wavevector along the y-axis
that is created with the
subroutine init()
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
0
10
20
30
40
50
60
70
80
90
Energy [eV]
Arbitrary
Units
Initial Energy Distribution created
with the subroutine init()
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
10
10
10
11
10
12
10
13
10
14
10
15
Energy [eV]
Scattering
Rate
[1/s]
acoustic
polar optical phonons
intervalley gamma to L
intervalley gamma to X
24. parameters initialization
readin()
scattering table construction
sc_table()
histograms calculation
histograms()
Free-Flight-Scatter
free_flight_scatter()
histograms calculation
histograms()
write data
write()
?
carriers initialization
init()
t t t
Time t exceeds maximum
simulation time tmax
yes
no
Optional
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
10
10
10
11
10
12
10
13
10
14
10
15
Energy [eV]
Scattering
Rate
[1/s]
acoustic
polar optical phonons
intervalley gamma to L
intervalley gamma to X
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Energy [eV]
Cumulative
rate
[1/s]
-6 -4 -2 0 2 4 6
x 10
8
0
5
10
15
20
25
30
35
40
Wavevector ky [1/m]
Arbitrary
Units
Initial Distribution of the
wavevector along the y-axis
that is created with the
subroutine init()
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
0
10
20
30
40
50
60
70
80
90
Energy [eV]
Arbitrary
Units
Initial Energy Distribution created
with the subroutine init()
25. Transient Data
0 1 2
x 10
-11
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10
5
time [s]
velocity
[m/s]
26. Steady-State
Results
0 1 2 3 4 5 6 7
x 10
5
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
x 10
5
Electric Field [V/m]
Drift
Velocity
[m/s]
0 1 2 3 4 5 6 7
x 10
5
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Electric Field [V/m]
Conduction
Band
Valley
Occupancy
gamma valley occupancy
L valley occupancy
X valley occupancy
k-vector
-valley
X-valley [100]
L-valley [111]
Conduction bands
Valence bands
Gunn Effect
27. Particle-Based Device Simulations
In a particle-based device simulation
approach the Poisson equation is
decoupled from the BTE over a short time
period dt smaller than the dielectric
relaxation time
Poisson and BTE are solved in a time-marching
manner
During each time step dt the electric field is
assumed to be constant (kept frozen)
28. Particle-Mesh Coupling
The particle-mesh coupling scheme consists of the
following steps:
- Assign charge to the Poisson solver mesh
- Solve Poisson’s equation for V(r)
- Calculate the force and interpolate it to the
particle locations
- Solve the equations of motion:
r
1 E
k
k
r
k
q
dt
d
t
E
dt
d
;
Laux, S.E., On particle-mesh coupling in Monte Carlo semiconductor device simulation,
Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on,
Volume 15, Issue 10, Oct 1996 Page(s):1266 - 1277
29. Assign Charge to the Poisson Mesh
1. Nearest grid point scheme
2. Nearest element cell scheme
3. Cloud in cell scheme
30. Force interpolation
The SAME METHOD that is used for the
charge assignment has to be used for the
FORCE INTERPOLATION:
,
r r r r
F E
i i p p
p
q W
xp-1 xp xp+1
e
p
x
w
p
x
w
p
p
p
e
p
p
p
x
V
V
x
V
V 1
1
2
1
p
E
31. Treatment of the Contacts
From the aspect of device physics, one can distinguish
between the following types of contacts:
(1) Contacts, which allow a current flow in and out of the
device
- Ohmic contacts: purely voltage or purely current
controlled
- Schottky contacts
(2) Contacts where only voltages can be applied
32. Calculation of the Current
The current in steady-state conditions is
calculated in two ways:
By counting the total number of particles that
enter/exit particular contact
By using the Ramo-Shockley theorem
according to which, in the channel, the current
is calculated using
1
( ),
N
x
i
e
I v i
dL
33. Current Calculated by Counting the Net Number of
Particles Exiting/Entering a Contact
Electrons that naturally came out in time interval dt (N1)
Electrons that were deleted (N2)
Electrons that were injected (N3)
dq = q(N1+N2-N3), q(t+dt)=q(t) + dq, current equals the slope of q(t) vs. t
Source Drain
Gate
Mesh node
Electron
Dopant
34. Device Simulation Results for MOSFETs:
Current Conservation
0
1000
2000
3000
4000
5000
6000
7000
1 1.5 2 2.5 3
source contact
drain contact
net
#
of
electrons
exiting/entering
contact
time [ps]
W
G
= 0.5 mm
(a)
0
0.1
0.2
0.3
0.4
0.5
0.6
0 50 100 150
Current
I
D
[mA/mm] Distance [nm]
(b)
VG=1.4 V, VD=1 V
Cumulative net number of particles
Entering/exiting a contact for a 50 nm
Channel length device
Drain contact
Source contact
Current calculated using Ramo-Schockley
formula
1
( ),
N
x
i
e
I v i
dL
X. He, MS, ASU, 2000.
35. Simulation Results for MOSFETs: Velocity
and Enery Along the Channel
0
5x10
6
1x10
7
1.5x10
7
2x10
7
2.5x10
7
0 50 100 150
Drift
velocity
[cm/s]
Distance [nm]
(a)
Mean Drift Velocity
Along the Channel
0
0.1
0.2
0.3
0.4
0.5
0.6
0 50 100 150
Average
energy
[eV]
Distance [nm]
(b)
Average Kinetic Energy
Along the Channel
VD = 1 V, VG = 1.2 V
Velocity overshoot effect observed
throughout the whole channel length of
the device – non-stationary transport.
For the bias conditions used average
electron energy is smaller that 0.6 eV
which justifies the use of non-parabolic
band model.
36. Simulation Results for MOSFETs:
IV Characteristics
0
0.1
0.2
0.3
0.4
0.5
0 0.2 0.4 0.6 0.8 1
Drain
current
[mA/mm]
Drain voltage V
D
[V]
1.2 V
0.8 V
1.0 V
Silvaco simulations
VG
= 1.0 V
VG
= 1.4 V
2D MCPS
VG
= 1.4 V
The differences between
the Monte Carlo and the
Silvaco simulations are
due to the following
reasons:
• Different transport
models used (non-
stationary transport is
taking place in this device
structure).
• Surface-roughness and
Coulomb scattering are
not included in the
theoretical model used in
the 2D-MCPS.
X. He, MS, ASU, 2000.
37. Simulation Results For SOI MESFET
Devices – Where are the Carriers?
Nd = 1019
Na =3-10x 1015
Nd = 1019
Si Substrate
Lg =60, 100nm
Lg =60, 100nm
Oxide Layer
Nd = 1019
Nd = 3-10x1015
Nd =1019
Si Substrate
Oxide Layer
Fig. 3.1 Schematic cross-sections of a) the SOI MOSFET and b) the SOI
MESFET devices that have been simulated.
Fig. 3.2. The electron distributions in c) the 60 nm SOI MOSFET and d) the 60 nm
SOI
MOSFET
SOI
MESFET
Applications:
Low-power RF electronics.
T.J. Thornton, IEEE Electron Dev. Lett., 8171 (1985).
Micropower circuits based
on weakly inverted
Implantable
cochlea and retina
Digital Watch
Pacemaker
MOSFETs
38. Proper Modeling of SOI MESFET Device
Gate current calculation:
WKB Approximation
Transfer Matrix Approach
for piece-wise linear
potentials
Interface-Roughness:
K-space treatment
Real-space treatment
Goodnick et al., Phys. Rev. B 32, 8171
(1985)
39. 10
7
10
8
10
9
10
10
10
11
10
12
0.1 1 10 100 1000
Lg =25nm simulated results
Lg =50nm simulated results
Lg = 90nm simulated results
Lg = 0.6um Experimental results
Lg = 50nm Projected Experimental results
Drain Current I
d
[ µA/µm]
Cutoff
Frequency
f
T
[Hz]
Output Characteristics and Cut-off
Frequency of a Si MESFET Device
-1000
-800
-600
-400
-200
0
200
400
-0.2 0 0.2 0.4 0.6 0.8 1
Vgs
= 0.3V
Vgs
= 0.4V
V
gs
=0.5V
V
gs
= 0.6V
I
d
[
µ
A/
µ
m]
V
ds
[Volts]
Tarik Khan, PhD, ASU, 2004.
40. Output Characteristics and Cut-off
Frequency of a Si MESFET Device
-1000
-800
-600
-400
-200
0
200
400
-0.2 0 0.2 0.4 0.6 0.8 1
Vgs
= 0.3V
Vgs
= 0.4V
V
gs
=0.5V
V
gs
= 0.6V
I
d
[
µ
A/
µ
m]
V
ds
[Volts]
10
7
10
8
10
9
10
10
10
11
10
12
0.1 1 10 100 1000
Lg =25nm simulated results
Lg =50nm simulated results
Lg = 90nm simulated results
Lg = 0.6um Experimental results
Lg = 50nm Projected Experimental results
Drain Current I
d
[ µA/µm]
Cutoff
Frequency
f
T
[Hz]
Tarik Khan, PhD, ASU, 2004.
41. Modeling of SOI Devices
When modeling SOI devices lattice
heating effects has to be accounted for
In what follows we discuss the following:
Comparison of the Monte Carlo, Hydrodynamic
and Drift-Diffusion results of Fully-Depleted SOI
Device Structures*
Impact of self-heating effects on the operation
of the same generations of Fully-Depleted SOI
Devices
*D. Vasileska. K. Raleva and S. M. Goodnick, IEEE Trans. Electron Dev., in press.
42. FD-SOI Devices:
Monte Carlo vs. Hydrodynamic vs. Drift-Diffusion
0 0.2 0.4 0.6 0.8 1
0
0.5
1
1.5
2
2.5
3
Drain Voltage [V]
Drain
Current
[mA/um]
DD SR
HD SR
Monte Carlo
0 0.2 0.4 0.6 0.8 1 1.2
0
0.5
1
1.5
2
2.5
Drain Voltage [V]
Drain
Current
[mA/um]
DD SR
HD SR
Monte Carlo
0 0.2 0.4 0.6 0.8 1 1.2 1.4
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Drain Voltage [V]
Drain
Current
[mA/um]
DD SR
HD SR
Monte Carlo
Source Drain
Gate oxide
BOX
tox
tsi
tBOX
LS Lgate LD
feature 14 nm 25 nm 90 nm
Tox 1 nm 1.2 nm 1.5 nm
VDD 1V 1.2 V 1.4 V
Overshoot
EB/HD
233% / 224% 139% / 126% 31% /21%
Overshoot EB/DD
with series resistance
153%/96% 108%/67% 39%/26%
Source/drain doping = 1020
cm-3
and 1019
cm-3
(series resistance (SR) case)
Channel doping = 1E18 cm-3
Overshoot= (IDHD-IDDD)/IDDD (%) at on-state
Silvaco ATLAS simulations performed by Prof. Vasileska.
90 nm
43. FD-SOI Devices:
Monte Carlo vs. Hydrodynamic vs. Drift-Diffusion
0 0.2 0.4 0.6 0.8 1
0
0.5
1
1.5
2
2.5
3
Drain Voltage [V]
Drain
Current
[mA/um]
DD SR
HD SR
Monte Carlo
0 0.2 0.4 0.6 0.8 1 1.2 1.4
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Drain Voltage [V]
Drain
Current
[mA/um]
DD SR
HD SR
Monte Carlo
Source Drain
Gate oxide
BOX
tox
tsi
tBOX
LS Lgate LD
feature 14 nm 25 nm 90 nm
Tox 1 nm 1.2 nm 1.5 nm
VDD 1V 1.2 V 1.4 V
Overshoot
EB/HD
233% / 224% 139% / 126% 31% /21%
Overshoot EB/DD
with series resistance
153%/96% 108%/67% 39%/26%
Source/drain doping = 1020
cm-3
and 1019
cm-3
(series resistance (SR) case)
Channel doping = 1E18 cm-3
Overshoot= (IDHD-IDDD)/IDDD (%) at on-state
Silvaco ATLAS simulations performed by Prof. Vasileska.
0 0.2 0.4 0.6 0.8 1 1.2
0
0.5
1
1.5
2
2.5
Drain Voltage [V]
Drain
Current
[mA/um]
DD SR
HD SR
Monte Carlo
25 nm
44. FD-SOI Devices:
Monte Carlo vs. Hydrodynamic vs. Drift-Diffusion
0 0.2 0.4 0.6 0.8 1 1.2
0
0.5
1
1.5
2
2.5
Drain Voltage [V]
Drain
Current
[mA/um]
DD SR
HD SR
Monte Carlo
0 0.2 0.4 0.6 0.8 1 1.2 1.4
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Drain Voltage [V]
Drain
Current
[mA/um]
DD SR
HD SR
Monte Carlo
Source Drain
Gate oxide
BOX
tox
tsi
tBOX
LS Lgate LD
feature 14 nm 25 nm 90 nm
Tox 1 nm 1.2 nm 1.5 nm
VDD 1V 1.2 V 1.4 V
Overshoot
EB/HD
233% / 224% 139% / 126% 31% /21%
Overshoot EB/DD
with series resistance
153%/96% 108%/67% 39%/26%
Source/drain doping = 1020
cm-3
and 1019
cm-3
(series resistance (SR) case)
Channel doping = 1E18 cm-3
Overshoot= (IDHD-IDDD)/IDDD (%) at on-state
Silvaco ATLAS simulations performed by Prof. Vasileska.
0 0.2 0.4 0.6 0.8 1
0
0.5
1
1.5
2
2.5
3
Drain Voltage [V]
Drain
Current
[mA/um]
DD SR
HD SR
Monte Carlo
14 nm
45. FD-SOI Devices:
Why Self-Heating Effect is Important?
1. Alternative materials (SiGe)
2. Alternative device designs (FD SOI, DG,
TG, MG, Fin-FET transistors
46. FD-SOI Devices:
Why Self-Heating Effect is Important?
dS
L ~
300nm
A. Majumdar, “Microscale Heat Conduction
in Dielectric Thin Films,” Journal of Heat
Transfer, Vol. 115, pp. 7-16, 1993.
47. Conductivity of Thin Silicon Films –
Vasileska Empirical Formula
300 400 500 600
20
40
60
80
Temperature (K)
Thermal
conductivity
(W/m/K)
experimental data
full lines: BTE predictions
dashed lines: empirical model
thin lines: Sondheimer
20nm
30nm
50nm
100nm
/2
3
0
0
2
( ) ( ) sin 1 exp cosh
2 ( )cos 2 ( )cos
a a z
z T d
T T
0
( ) (300/ )
T T
0 2
135
( ) W/m/K
T
a bT cT
0 2 4 6 8 10
10
5
7.5
10
12.5
15
17.5
20
20
Distance from Si/gate oxide interface (nm)
Thermal
conductivity
(W/m-K)
300K
400K
600K
Si
BOX
10nm
0 20 40 60 80 100
0
10
20
30
40
50
60
70
80
80
Distance from Si/gate oxide interface (nm)
Thermal
conductivity
(W/m-K)
20nm
30nm
50nm
100nm
48. Particle-Based Device Simulator That
Includes Heating
Average and smooth: electron
density, drift velocity and electron
energy at each mesh point
end of MCPS
phase?
Acoustic and Optical Phonon
Energy Balance Equations Solver
end of
simulation?
end
no
yes
Define device structure
Generate phonon temperature
dependent scattering tables
Initial potential, fields, positions and
velocities of carriers
t = 0
t = t + t
Transport Kernel (MC phase)
Field Kernel (Poisson Solver)
t = n t?
yes
Average and smooth: electron
density, drift velocity and electron
energy at each mesh point
end of MCPS
phase?
end of MCPS
phase?
Acoustic and Optical Phonon
Energy Balance Equations Solver
end of
simulation?
end of
simulation?
end
no
yes
Define device structure
Generate phonon temperature
dependent scattering tables
Initial potential, fields, positions and
velocities of carriers
t = 0
t = t + t
Transport Kernel (MC phase)
Field Kernel (Poisson Solver)
t = n t?
Define device structure
Generate phonon temperature
dependent scattering tables
Initial potential, fields, positions and
velocities of carriers
t = 0
t = t + t
Transport Kernel (MC phase)
Field Kernel (Poisson Solver)
t = n t?
t = n t?
yes
50. Higher Order Effects Inclusion in Particle-
Based Simulators
Degeneracy – Pauli Exclusion Principle
Short-Range Coulomb Interactions
Fast Multipole Method (FMM)
V. Rokhlin and L. Greengard, J. Comp. Phys., 73, pp. 325-348
(1987).
Corrected Coulomb Approach
W. J. Gross, D. Vasileska and D. K. Ferry, IEEE Electron Device
Lett. 20, No. 9, pp. 463-465 (1999).
P3M Method
Hockney and Eastwood, Computer Simulation Using Particles.
55. MOSFETs - Standard Characteristics
0
50
100
150
200
250
300
350
40 60 80 100 120 140
V
D
=1.0 [V], V
G
=1.0 [V]
V
D
=0.5 [V], V
G
=1.0 [V]
Electron
energy
[meV]
Distance [nm]
L
G
= 80 nm
0
5x10
6
1x10
7
1.5x10
7
2x10
7
40 60 80 100 120 140
V
D
=0.5 [V], V
G
=1.0 [V]
V
D
=1.0 [V], V
G
=1.0 [V]
Drift
velocity
[cm/s]
Distance [nm]
L
G
=80 nm
(a)
The average energy of the carriers increases when going from the
source to the drain end of the channel. Most of the phonon scattering
events occur at the first half of the channel.
Velocity overshoot occurs near the drain end of the channel. The
sharp velocity drop is due to e-e and e-i interactions coming from the
drain.
W. J. Gross, D. Vasileska and D. K. Ferry, "3D Simulations of Ultra-Small MOSFETs with Real-Space
Treatment of the Electron-Electron and Electron-Ion Interactions," VLSI Design, Vol. 10, pp. 437-452 (2000).
56. MOSFETs - Role of the E-E and E-I
0
100
200
300
400
100 110 120 130 140 150 160 170 180
with e-e and e-i
mesh force only
Electron
energy
[meV]
Length [nm]
V
D
=1 V, V
G
=1 V
channel drain
0
100
200
300
400
500
600
700
800
0.12 0.13 0.14 0.15 0.16 0.17 0.18
Energy
[meV]
Length [nm]
0
100
200
300
400
500
600
700
800
0.12 0.13 0.14 0.15 0.16 0.17 0.18
Length [nm]
Energy
[meV]
mesh force
only
with e-e and e-i
Individual electron
trajectories over
time
-1x10
7
-5x10
6
0
5x10
6
1x10
7
1.5x10
7
2x10
7
2.5x10
7
0 40 80 120 160
with e-e and e-i
mesh force only
Drift
velocity
[cm/s]
Length [nm]
V
D
=V
G
=1.0 V
source drain
channel
57. MOSFETs - Role of the E-E and E-I
Mesh force only With e-e and e-i
Short-range e-e and e-i interactions push some
of the electrons towards higher energies
10
-3
10
-2
0 50 100 150 200 250 300 350 400
source
channel
drain
Electron
distribution
(arb.
units)
Energy [meV]
V
G
=0.5 V, V
D
=0.8 V
10
-3
10
-2
0 50 100 150 200 250 300 350 400
Source
Channel
Drain
Electron
distribution
(arb.
units)
Energy [meV]
V
G
=0.5 V, V
D
=0.8 V
D. Vasileska, W. J. Gross, and D. K. Ferry, "Monte-Carlo particle-based simulations of deep-submicron n-
MOSFETs with real-space treatment of electron-electron and electron-impurity interactions," Superlattices
and Microstructures, Vol. 27, No. 2/3, pp. 147-157 (2000).
58. Degradation of Output Characteristics
0
10
20
30
40
50
60
70
80
0.0 0.2 0.4 0.6 0.8 1.0 1.2
with corrected Coulomb
mesh force only
Drain
current
I
D
[mA]
Drain voltage V
D
[V]
increasing V
G
LG = 35 nm, WG = 35 nm, NA = 5x1018 cm-3,
Tox = 2 nm, VG = 11.6 V (0.2 V)
The short range e -e and e -i interactions have significant influence on
the device output characteristics.
There is almost a factor of two decrease in current when these two inte-
raction terms are considered.
LG = 50 nm, WG = 35 nm, NA = 5x1018 cm-3
Tox = 2 nm, VG = 11.6 V (0.2 V)
0
10
20
30
40
50
60
70
80
0 0.2 0.4 0.6 0.8 1 1.2
with corrected Coulomb
mesh force only
Drain
current
I
D
[mA]
Drain voltage V
D
[V]
increasing V
G
W. J. Gross, D. Vasileska and D. K. Ferry, "Ultra-small MOSFETs: The importance of the full Coulomb
interaction on device characteristics," IEEE Trans. Electron Devices, Vol. 47, No. 10, pp. 1831-1837 (2000).
59. Mizuno result:
(60% of the fluctuations)
Stolk et al. result:
(100% of the fluctuations)
Fluctuations in the
surface potential
Fluctuations in the
electric field
Depth-
Distribution
of the charges
60. MOSFETs - Discrete Impurity Effects
0
20
40
60
80
100
0 1 2 3 4 5
s
Vth
=> approach 1
s
Vth
=> approach 2
s
Vth
=> our simulation results
s
Vth
[mV]
Oxide thickness T
ox
[nm]
0
5
10
15
20
25
30
35
40
1x10
18
3x10
18
5x10
18
7x10
18
s
Vth
=> approach 1
s
Vth
=> approach 2
s
Vth
=> our simulation results
s
Vth
[mV]
Doping density N
A
[cm
-3
]
10
20
30
40
50
60
20 40 60 80 100 120 140
s
Vth
=> approach 1
s
Vth
=> approach 2
s
Vth
=> our simulation results
s
Vth
[mV] Device width [nm]
eff
eff
A
ox
ox
A
B
Si
B
B
Si
Vth
W
L
N
T
N
q
q
/
T
k
q 4
4 3
4
3
4
s
Approach 2 [2]:
s
i
A
B
B
eff
eff
A
ox
ox
B
Si
Vth
n
N
ln
q
T
k
;
W
L
N
T
q 4
4 3
2
Approach 1 [1]:
[1] T. Mizuno, J. Okamura, and A. Toriumi, IEEE Trans. Electron Dev. 41, 2216 (1994).
[2] P. A. Stolk, F. P. Widdershoven, and D. B. Klaassen, IEEE Trans. Electron Dev. 45, 1960
(1998).
61. 0
20
40
60
80
100
120
140
160
180
200
160
170
180
190
200
210
220
230
240
250
260
270
Number of Atoms in Channel
Number
of
Devices
5 samples at
maximum
5 samples at
minimum
5 samples of average
Depth Correlation of sV
T
To understand the role that the position
of the impurity atoms plays on the
threshold voltage fluctuations, statistical
ensembles of 5 devices from the low-end,
center and the high-end of the
distribution were considered.
Significant correlation was observed
between the threshold voltage and the
number of atoms that fall within the first 15
nm depth of the channel.
0.9
1
1.1
1.2
1.3
1.4
160 180 200 220 240 260 280 300
Threshold
voltage
[V]
Number of channel dopant atoms
low-end
center
high-end
(a)
L
G
=50 nm, W
G
=35 nm
N
A
=5x10
18
cm
-3
, T
ox
=3 nm
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25
V
T
correlation
Depth [nm]
depth
Moving slab
range
ND ND
(b) L
G
=50 nm, W
G
=35 nm, T
ox
=3 nm
N
A
=5x10
18
cm
-3
Number of atoms in the channel
Number
of
devices
Number of atoms in the channel
Depth [nm]
V
T
correlation
Threshold
voltage
[V]
62. Fluctuations in High-Field
Characteristics
0
0.2
0.4
0.6
0.8
1
0 5 10 15 20 25
average velocity
correlation
drain current
correlation
Correlation
Depth [nm]
(c)
LG
=50 nm
WG
=35 nm
Tox
=3 nm
NA
=5x10
18
cm
-3
Impurity distribution in the channel also
affects the carrier mobility and saturation
current of the device.
Significant correlation was observed
between the drift velocity (saturation
current) and the number of atoms that fall
within the first 10 nm depth of the
channel.
0
5 10
6
1 10
7
1.5 10
7
160 180 200 220 240 260 280
Drift
velocity
[cm/s]
Number of channel dopant atoms
(a)
low-end
center
high-end
VG
=1.5 V, VD
=1 V
LG
=50 nm, WG
=35 nm
NA
=5x10
18
cm
-3
0
5
10
15
20
160 180 200 220 240 260 280
Drain
current
[mA]
Number of channel dopant atoms
low-end
center
high-end
(b)
Number of atoms in the channel
Drift
velocity
[cm/s]
Number of atoms in the channel
Depth [nm]
Correlation
Drain
current
[mA]
VG = 1.5 V, VD = 1 V
65. Results for SOI Device
Size Quantization Effect (Effective Potential):
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
2 4 6 8 10 12 14 16
Channel Width [nm]
Threshold
Voltage
[V]
Experimental
Simulation
S. S. Ahmed and D. Vasileska, “Threshold voltage shifts in narrow-width SOI devices due to
quantum mechanical size-quantization effect”, Physica E, Vol. 19, pp. 48-52 (2003).
66. Results for SOI Device
Due to the unintentional dopant both the electrostatics
and the transport are affected.
-10000
10000
30000
50000
70000
90000
110000
130000
0 20 40 60 80
Distance Along the Channel [nm]
Average
Velocity
[m/s]
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
Average
Kinetic
Energy
[eV]
Velocity
Energy
Dip due to the presence of
the impurity. This affects the
transport of the carriers.
67. Results for SOI Device
Unintentional Dopant:
D. Vasileska and S. S. Ahmed, “Narrow-Width SOI Devices: The Role of Quantum Mechanical
Size Quantization Effect and the Unintentional Doping on the Device Operation”, IEEE
Transactions on Electron Devices, Volume 52, Issue 2, Feb. 2005 Page(s):227 – 236.
68. Results for SOI Device
Channel Width = 10 nm
VG = 1.0 V
VD = 0.1 V
32.54%
47.62%
33.01%
26.98%
42.85%
27.18%
11.90%
26.19%
11.11%
0
1
2
3
4
5
6
7
8
9
10
0 10 20 30 40 50
Distance Along the Channel [nm]
Distance
Along
the
Width
[nm]
.
Source
Drain 34.13%
47.62%
42.06%
16.66%
42.85%
26.19%
9.93%
26.19%
19.46%
0
1
2
3
4
5
6
7
0 10 20 30 40 50
Distance Along the Channel [nm]
Distance
Along
the
Depth
[nm]
.
Source
Drain
69. Results for SOI Device
86.30%
96.76%
86.52%
87.39%
96.09%
86.96%
69.57%
88.26%
67.39%
0
1
2
3
4
5
0 10 20 30 40 50
Distance Along the Channel [nm]
Distance
Along
the
Width
[nm]
Source
Drain
81.09%
96.76%
88.48%
79.78%
96.09%
88.26%
59.78%
88.26%
76.09%
0
1
2
3
4
5
6
7
0 10 20 30 40 50
Distance Along the Channel [nm]
Distance
Along
the
Depth
[nm]
Source
Drain
Channel Width = 5 nm
VG = 1.0 V
VD = 0.1 V
70. Results for SOI Device
Impurity located at the very source-end, due to the availability of Increasing
number of electrons screening the impurity ion, has reduced impact on the
overall drain current.
0%
10%
20%
30%
40%
50%
60%
0 10 20 30 40 50
Distance Along the Channel [nm]
Current
Reduction
Impurity position varying along the
center of the channel
V G = 1.0 V
V D = 0.2 V
Source end Drain end
71. Results for SOI Device
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
2 4 6 8 10 12 14 16
Channel Width [nm]
Threshold
Voltage
[V]
Experimental
Simulation (QM)
Discrete single dopants
D. Vasileska and S. S. Ahmed, IEEE Transactions on Electron Devices, Volume 52, Issue 2, Feb. 2005
Page(s):227 – 236.
S. Ahmed, C. Ringhofer and D. Vasileska, Nanotechnology, IEEE Transactions on, Volume 4, Issue 4, July 2005
Page(s):465 – 471.
D. Vasileska, H. R. Khan and S. S. Ahmed, International Journal of Nanoscience, Invited Review Paper, 2005.
72. Results for SOI Device
Electron-Electron Interactions:
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
1.E+01
1.E+02
0 0.2 0.4 0.6 0.8 1
Electron Kinetic Energy [eV]
Distribution
Function
[a.u.]
PM
FMM
V G = 1.0 V
V D = 0.3 V
0.0E+00
5.0E+04
1.0E+05
1.5E+05
2.0E+05
2.5E+05
3.0E+05
0 20 40 60 80 100
Distance Along the Channel [nm]
Electron
Velocity
[m/s]
PM
FMM
V G = 1.0 V
V D = 0.3 V
Source Drain
D. Vasileska and S. S. Ahmed, IEEE Transactions on Electron Devices, Volume 52, Issue 2, Feb. 2005
Page(s):227 – 236.
S. Ahmed, C. Ringhofer and D. Vasileska, Nanotechnology, IEEE Transactions on, Volume 4, Issue 4, July 2005
Page(s):465 – 471.
D. Vasileska, H. R. Khan and S. S. Ahmed, International Journal of Nanoscience, Invited Review Paper, 2005.
73. Summary
Particle-based device simulations are the most desired
tool when modeling transport in devices in which velocity
overshoot (non-stationary transport) exists
Particle-based device simulators are rather suitable for
modeling ballistic transport in nano-devices
It is rather easy to include short-range electron-electron
and electron-ion interactions in particle-based device
simulators via a real-space molecular dynamics routine
Quantum-mechanical effects (size quantization and
density of states modifications) can be incorporated in
the model quite easily with the assumption of adiabatic
approximation and solution of the 1D or 2D Schrodinger
equation in slices along the channel section of the device