This document outlines the content to be covered in a lecture on surfaces and interfaces. It will discuss the history and importance of studying surfaces, key concepts like Miller indices and polar surfaces, surface energetics and electronic structure, the role of surfaces in photovoltaics, interface classifications and formation, and methods for calculating surface and interface properties using density functional theory. The lecture emphasizes that understanding surfaces and interfaces is crucial in fields like semiconductor technology since "the interface is the device".
Surface and Interface • When phases exist together, the boundary between two of them is known as an interface. • The properties of the molecules forming the interface are often sufficiently different from those in the bulk of each phase. • The term surface is used when referring to either a gas–solid or a gas–liquid interface.
Surface and Interface • When phases exist together, the boundary between two of them is known as an interface. • The properties of the molecules forming the interface are often sufficiently different from those in the bulk of each phase. • The term surface is used when referring to either a gas–solid or a gas–liquid interface.
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Energy bands consisting of a large number of closely spaced energy levels exist in crystalline materials. The bands can be thought of as the collection of the individual energy levels of electrons surrounding each atom. The wavefunctions of the individual electrons, however, overlap with those of electrons confined to neighboring atoms. The Pauli exclusion principle does not allow the electron energy levels to be the same so that one obtains a set of closely spaced energy levels, forming an energy band. The energy band model is crucial to any detailed treatment of semiconductor devices. It provides the framework needed to understand the concept of an energy bandgap and that of conduction in an almost filled band as described by the empty states.
In this talk I will discuss different approximations in DFT: pseduo-potentials, exchange correlation functions.
The presentation can be downloaded here:
http://www.attaccalite.com/wp-content/uploads/2022/03/dft_approximations.odp
Electrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materials
The Utility of Zeta Potential Measurements in the Characterization of CMP Slu...HORIBA Particle
Guest speaker Dr. David Fairhurst joins HORIBA Scientific to discuss the important role zeta potential (ZP) plays in the performance of CMP slurries. In this webinar we will first review and discuss the importance of the iso-electric point of metal-oxide polishing agents and the role of slurry pH and then we will examine the effect of slurry fluid chemistry using ZP measurements to characterize the chemically-modulated development of surface charge of such polishing agents during aqueous polishing.
While particle size has been long recognized an important metric, the most fundamental and critical aspect of the CMP slurry system is the molecular interaction between the suspended particles and the medium at the interface. It is such an interaction, along with the particle–particle interactions, that determines the dynamic properties of the dispersion and strongly affect the performance of the slurry. In polishing, it is generally accepted that the removal rate is directly related to the availability of the surface hydroxyl groups on the abrasive particles. Thus, understanding the role of surface charge effects is important in optical finishing. The zeta potential is a parameter (symbol ζ), which is related to the surface charge (vide the interfacial chemistry), a property that all materials possess, or acquire, when suspended in a fluid; ZP measurements have long been recognized as a surrogate function of particle surface chemistry.
Magnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materialsMagnetic properties of materials
Energy bands consisting of a large number of closely spaced energy levels exist in crystalline materials. The bands can be thought of as the collection of the individual energy levels of electrons surrounding each atom. The wavefunctions of the individual electrons, however, overlap with those of electrons confined to neighboring atoms. The Pauli exclusion principle does not allow the electron energy levels to be the same so that one obtains a set of closely spaced energy levels, forming an energy band. The energy band model is crucial to any detailed treatment of semiconductor devices. It provides the framework needed to understand the concept of an energy bandgap and that of conduction in an almost filled band as described by the empty states.
In this talk I will discuss different approximations in DFT: pseduo-potentials, exchange correlation functions.
The presentation can be downloaded here:
http://www.attaccalite.com/wp-content/uploads/2022/03/dft_approximations.odp
Electrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materialsElectrical properties of materials
The Utility of Zeta Potential Measurements in the Characterization of CMP Slu...HORIBA Particle
Guest speaker Dr. David Fairhurst joins HORIBA Scientific to discuss the important role zeta potential (ZP) plays in the performance of CMP slurries. In this webinar we will first review and discuss the importance of the iso-electric point of metal-oxide polishing agents and the role of slurry pH and then we will examine the effect of slurry fluid chemistry using ZP measurements to characterize the chemically-modulated development of surface charge of such polishing agents during aqueous polishing.
While particle size has been long recognized an important metric, the most fundamental and critical aspect of the CMP slurry system is the molecular interaction between the suspended particles and the medium at the interface. It is such an interaction, along with the particle–particle interactions, that determines the dynamic properties of the dispersion and strongly affect the performance of the slurry. In polishing, it is generally accepted that the removal rate is directly related to the availability of the surface hydroxyl groups on the abrasive particles. Thus, understanding the role of surface charge effects is important in optical finishing. The zeta potential is a parameter (symbol ζ), which is related to the surface charge (vide the interfacial chemistry), a property that all materials possess, or acquire, when suspended in a fluid; ZP measurements have long been recognized as a surrogate function of particle surface chemistry.
UCSD NANO 266 Quantum Mechanical Modelling of Materials and Nanostructures is a graduate class that provides students with a highly practical introduction to the application of first principles quantum mechanical simulations to model, understand and predict the properties of materials and nano-structures. The syllabus includes: a brief introduction to quantum mechanics and the Hartree-Fock and density functional theory (DFT) formulations; practical simulation considerations such as convergence, selection of the appropriate functional and parameters; interpretation of the results from simulations, including the limits of accuracy of each method. Several lab sessions provide students with hands-on experience in the conduct of simulations. A key aspect of the course is in the use of programming to facilitate calculations and analysis.
UCSD NANO 266 Quantum Mechanical Modelling of Materials and Nanostructures is a graduate class that provides students with a highly practical introduction to the application of first principles quantum mechanical simulations to model, understand and predict the properties of materials and nano-structures. The syllabus includes: a brief introduction to quantum mechanics and the Hartree-Fock and density functional theory (DFT) formulations; practical simulation considerations such as convergence, selection of the appropriate functional and parameters; interpretation of the results from simulations, including the limits of accuracy of each method. Several lab sessions provide students with hands-on experience in the conduct of simulations. A key aspect of the course is in the use of programming to facilitate calculations and analysis.
Photoelectron spectroscopy
- a single photon in/ electron out process
• X-ray Photoelectron Spectroscopy (XPS)
- using soft x-ray (200-2000 eV) radiation to
examine core-levels.
• Ultraviolet Photoelectron Spectroscopy (UPS)
- using vacuum UV (10-45 eV) radiation to
examine valence levels.
This Lecture includes the Resistivity survey, field procedure, application advantage, limitaion, Apparant resistivity, VES (Vertical Electrical Sounding), Resistivity Profiling and IP Survey in brief.
This presentation presents a review of novel technology which provides a promising solution for designing self-powered microsystems. Micro-Electro Mechanical System (MEMS) energy harvesting is an emerging alternative for scavenging energy from natural sources. It has extensive potential in wireless sensor applications to provide a natural energy source that is essentially inexhaustible. It is an increasingly attractive alternative to costly batteries. This essentially free energy source is available maintenance-free throughout the lifetime of the application. Many systems, such as wireless sensor networks, portable electronics and cell phones, can use this technology as a power source. Although some types of MEMS, such as electro-magnetic MEMS, electrostatic MEMS, and piezoelectric MEMS, are used to provide energy in various applications, they have several technical barriers that limit their applications, including low efficiency, issues of scaling, and high cost.Novel MEMS solar energy harvesting technology is scalable and also easily integrated in microsystems. The RF MEMS design not only has to provide functional efficiency, but also must work within the limits of maximum charge and discharge conversion efficiency. The energy harvesting technologies currently available which utilizes RF MEMS to convert solar energy into charge, can achieve better benefits than photovoltaic cells. In this presentation the design,fabrication, testing and evaluation of RF MEMS and its working limits in charging and discharging is illustrated.
Microfluidic Flow Control using Magnetohydrodynamics KayDrive
Fluid manipulation in microfluidic devices is one of the main areas of research interest for the fabrication of Lab-On-a-Chip devices. From the many methods that have been applied to this problem, one of the most promising is employing Magnetohydrodynamic principles which allow for elegant and versatile designs. A microchip is designed for fluid flow control that uses MHD for pumping the fluid through a microchannel. Simulation of the design is performed in COMSOL and the velocity profile of the fluid is obtained. The microchip is fabricated, and experiments are performed by measuring the flow rate of a conducting fluid as it is pumped by the Lorentz force. The experimental results are then compared with the simulation results to compare the performance of the device to theoretical computations.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
BREEDING METHODS FOR DISEASE RESISTANCE.pptxRASHMI M G
Plant breeding for disease resistance is a strategy to reduce crop losses caused by disease. Plants have an innate immune system that allows them to recognize pathogens and provide resistance. However, breeding for long-lasting resistance often involves combining multiple resistance genes
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
1. Surfaces
and
Interfaces
Microscopic
mechanisms
and
macroscopic
consequences
Dr.
Keith
T.
Butler
Department
of
Chemistry
k.t.butler@bath.ac.uk
“God
made
the
bulk;
surfaces
were
invented
by
the
devil”
Wolfgang
Pauli
2. Content
• Background
and
history
of
surfaces
– History
– Importance
• Important
concepts
for
surface
definiEons
– Energy-‐band-‐alignment
diagram
– Miller
indices
– Tasker
notaEon
– Polar
surfaces
• Surface
energeEcs
and
electronic
structure
– Bond
breaking
approximaEon
– Surface
Tamm
states
• Surfaces
in
PV
– Trapping
– PassivaEon
• Interface
classificaEons
and
formaEon
• Strain
and
supercells
• WeKng
angle
and
cohesion
• Coherent/Semi-‐coherent/Incoherent
• Interfaces
in
PV
• SchoMky
barrier
/
Ohmic
contacts
• Charge
neutrality
level
• Band
Alignment
in
PV
• Work
funcEons
and
electron
energies
• Measuring
work
funcEons
• CalculaEng
work
funcEons
• Bulk/surface
contribuEons
• Work
funcEon
engineering
• PracEcal
examples
• CalculaEon
of
surface/interface
energy
in
DFT
• Band
alignment
from
DFT
SURFACES
INTERFACES
5. Benjamin
Franklin
and
the
old
wives
tale
“[T]he oil, though not more than a teaspoonful, produced an
instant calm over a space several yards square which spread
amazingly and extended itself gradually till it reached the lee side,
making all that quarter of the pond, perhaps half an acre, as
smooth as a looking glass.”
6. The
study
of
surfaces
• Mostly
atoms
are
not
at
the
surface
BULK
Surface
7. The
study
of
surfaces
&
interfaces
“The interface is the device”
Herbert
Kroemer
Nobel
prize
in
Physics
2000
“For
developing
semiconductor
heterostructures
used
in
high-‐speed-‐
and
opto-‐electronics"
8. Surfaces
in
PV
Charge
separa?on
Extrac?on
of
carriers
Recombina?on
Contact
resistance
hMp://www.pveducaEon.org
9. Content
• Background
and
history
of
surfaces
– History
– Importance
• Important
concepts
for
surface
definiEons
– Energy-‐band-‐alignment
diagram
– Miller
indices
– Tasker
notaEon
– Polar
surfaces
• Surface
energeEcs
and
electronic
structure
– Bond
breaking
approximaEon
– Surface
Tamm
states
• Surfaces
in
PV
– Trapping
– PassivaEon
• Interface
classificaEons
and
formaEon
• Strain
and
supercells
• WeKng
angle
and
cohesion
• Coherent/Semi-‐coherent/Incoherent
• Interfaces
in
PV
• SchoMky
barrier
/
Ohmic
contacts
• Charge
neutrality
level
• Band
Alignment
in
PV
• Work
funcEons
and
electron
energies
• Measuring
work
funcEons
• CalculaEng
work
funcEons
• Bulk/surface
contribuEons
• Work
funcEon
engineering
• PracEcal
examples
• CalculaEon
of
surface/interface
energy
in
DFT
• Band
alignment
from
DFT
SURFACES
INTERFACES
10. Energy-‐band-‐diagrams
Valence
Band
(Occupied
states)
ConducEon
Band
(Unoccupied
states)
Vacuum
level
Band-‐gap
Electron
Affinity
IonisaEon
potenEal
12. Energy-‐band-‐diagrams
“If, in discussing a semiconductor
problem, you cannot draw an
Energy-Band-Diagram, this shows
that you don’t know what you are
talking about”
“If you can draw one, but don’t, then
your audience won’t know what you
are talking about.”
21. ClassificaEon
IdenEfy
intercepts
FracEonal
coordinates
of
intercepts
If
fracEons
result
in
step
(ii)
then
round
up
all
indices
by
mulEplicaEon;
e.g.
(1/3,0,1)
-‐>
(1,0,3)
NegaEve
numbers
are
indicated
by
an
over-‐bar
23. The
Polar
Catastrophe
Type
III
PotenEal
Energy
P
W
Tasker
1979
J.
Phys.
C:
Solid
State
Phys.
12
4977
24. Examples
of
Polar
Surfaces
• A
polar
surface
can
exist
–
with
modificaEons.
• Zincblende
(100)
• Mechanisms
for
stabilisaEon:
– Change
in
stoiciometry
in
surface
layers
– AdsorpEon
of
ions
on
the
surfaces
– Electron
redistribuEon
2D
electron
gas
C.
Noguera
,
J.
Phys.:
Condens.
MaMer
12
R367
σ j
j=1
m
∑ = −
σm+1
2
(−1)m
−
R2 − R1
R2 + R1
#
$
%
&
'
(
25. Content
• Background
and
history
of
surfaces
– History
– Importance
• Important
concepts
for
surface
definiEons
– Energy-‐band-‐alignment
diagram
– Miller
indices
– Tasker
notaEon
– Polar
surfaces
• Surface
energeEcs
and
electronic
structure
– Bond
breaking
approximaEon
– Surface
Tamm
states
• Surfaces
in
PV
– Trapping
– PassivaEon
• Interface
classificaEons
and
formaEon
• Strain
and
supercells
• WeKng
angle
and
cohesion
• Coherent/Semi-‐coherent/Incoherent
• Interfaces
in
PV
• SchoMky
barrier
/
Ohmic
contacts
• Charge
neutrality
level
• Band
Alignment
in
PV
• Work
funcEons
and
electron
energies
• Measuring
work
funcEons
• CalculaEng
work
funcEons
• Bulk/surface
contribuEons
• Work
funcEon
engineering
• PracEcal
examples
• CalculaEon
of
surface/interface
energy
in
DFT
• Band
alignment
from
DFT
SURFACES
INTERFACES
29. Content
• Background
and
history
of
surfaces
– History
– Importance
• Important
concepts
for
surface
definiEons
– Energy-‐band-‐alignment
diagram
– Miller
indices
– Tasker
notaEon
– Polar
surfaces
• Surface
energeEcs
and
electronic
structure
– Bond
breaking
approximaEon
– Surface
Tamm
states
• Surfaces
in
PV
– Trapping
– PassivaEon
• Interface
classificaEons
and
formaEon
• Strain
and
supercells
• WeKng
angle
and
cohesion
• Coherent/Semi-‐coherent/Incoherent
• Interfaces
in
PV
• SchoMky
barrier
/
Ohmic
contacts
• Charge
neutrality
level
• Band
Alignment
in
PV
• Work
funcEons
and
electron
energies
• Measuring
work
funcEons
• CalculaEng
work
funcEons
• Bulk/surface
contribuEons
• Work
funcEon
engineering
• PracEcal
examples
• CalculaEon
of
surface/interface
energy
in
DFT
• Band
alignment
from
DFT
SURFACES
INTERFACES
30. Surface
recombinaEon
• Characterised
by
capture
and
release
rates
of
carriers
and
energy
of
state
RSE
RSH
RSE
RSH
34. Content
• Background
and
history
of
surfaces
– History
– Importance
• Important
concepts
for
surface
definiEons
– Energy-‐band-‐alignment
diagram
– Miller
indices
– Tasker
notaEon
– Polar
surfaces
• Surface
energeEcs
and
electronic
structure
– Bond
breaking
approximaEon
– Surface
Tamm
states
• Surfaces
in
PV
– Trapping
– PassivaEon
• Interface
classificaEons
and
formaEon
• Strain
and
supercells
• WeKng
angle
and
cohesion
• Coherent/Semi-‐coherent/Incoherent
• Interfaces
in
PV
• SchoMky
barrier
/
Ohmic
contacts
• Charge
neutrality
level
• Band
Alignment
in
PV
• Work
funcEons
and
electron
energies
• Measuring
work
funcEons
• CalculaEng
work
funcEons
• Bulk/surface
contribuEons
• Work
funcEon
engineering
• PracEcal
examples
• CalculaEon
of
surface/interface
energy
in
DFT
• Band
alignment
from
DFT
SURFACES
INTERFACES
36. Interface
thermodynamics
• Interface
energy
related
to
weKng
angle.
σ1v
σ2v
σ12
MW
Finnis
1996
J.
Phys:
Condens.
Ma4er.
8
5811
37. LaKce
matching
• Depends
on
laKce
parameters
of
the
two
phases
• Determines
interface
strain;
large
contribuEon
to
interface
energy
a
b
38. Coherent
Interface
Interface
laKce
planes
must
match.
The
same
atomic
configuraEon
across
the
interface.
Examples:
CuSi
alloys
GaAs/AlAs
InAs/GaAs
Ge/Si
PbTe/CdTe
The
energy
of
coherent
interfaces:
Mismatching
bond
energy
Strain
energy
is
negligible
Energy
0
–
200
mJ/m^2
39. Semi-‐coherent
Interface
When
strains
are
sufficiently
large.
EnergeEcally
favorable
to
to
form
misfit
dislocaEons
at
interfaces.
Examples:
InAs/GaAs
The
energy
of
semi-‐coherent
interfaces:
Strain
plus
chemical
bonding
Energy
200
–
500
mJ/m^2
40. Incoherent
interface
Very
different
configuraEons
on
either
side
of
the
interface.
OR
laKce
constants
>
25%
difference.
Examples:
High
angle
grain
boundaries
Inclusions
in
alloys
The
energy
of
incoherent
interfaces:
Very
large
structural
contribuEon.
Energy
500
-‐1000
mJ/m^2
41. Content
• Background
and
history
of
surfaces
– History
– Importance
• Important
concepts
for
surface
definiEons
– Energy-‐band-‐alignment
diagram
– Miller
indices
– Tasker
notaEon
– Polar
surfaces
• Surface
energeEcs
and
electronic
structure
– Bond
breaking
approximaEon
– Surface
Tamm
states
• Surfaces
in
PV
– Trapping
– PassivaEon
• Interface
classificaEons
and
formaEon
• Strain
and
supercells
• WeKng
angle
and
cohesion
• Coherent/Semi-‐coherent/Incoherent
• Interfaces
in
PV
• SchoMky
barrier
/
Ohmic
contacts
• Charge
neutrality
level
• Band
Alignment
in
PV
• Work
funcEons
and
electron
energies
• Measuring
work
funcEons
• CalculaEng
work
funcEons
• Bulk/surface
contribuEons
• Work
funcEon
engineering
• PracEcal
examples
• CalculaEon
of
surface/interface
energy
in
DFT
• Band
alignment
from
DFT
SURFACES
INTERFACES
42. Ohmic
Contacts
in
PV
• Minimising
losses
in
PV
• V
∝
I
• Ideal
Ohmic
contacts
will
not
produce
potenEal
barriers
• Ideal
contact
all
Fermi
levels
align
44. Band
Bending
The
SchoMky
limit.
SchoMky
barrier
–
limits
charge
transport
across
the
interface.
Contact
resistance
depends
exponenEally
on
the
SchoMky
barrier.
46. ConsideraEons
for
devices
n-‐type
p-‐type
Space
charge
PosiEve
NegaEve
Metal
work
funcEon
Small
/
shallow
Large
/
deep
Examples
Li,
Na,
Ca,
K,
Au,
Ag,
Fe
47. Charge
Neutrality
Level/Surface
States
States
in
the
gap
of
the
semiconductor.
Can
result
in
addiEonal
charge
transfer.
New
local
charge
region.
Region
~
0.2
–
0.3
nm
49. Content
• Background
and
history
of
surfaces
– History
– Importance
• Important
concepts
for
surface
definiEons
– Energy-‐band-‐alignment
diagram
– Miller
indices
– Tasker
notaEon
– Polar
surfaces
• Surface
energeEcs
and
electronic
structure
– Bond
breaking
approximaEon
– Surface
Tamm
states
• Surfaces
in
PV
– Trapping
– PassivaEon
• Interface
classificaEons
and
formaEon
• Strain
and
supercells
• WeKng
angle
and
cohesion
• Coherent/Semi-‐coherent/Incoherent
• Interfaces
in
PV
• SchoMky
barrier
/
Ohmic
contacts
• Charge
neutrality
level
• Band
Alignment
in
PV
• Work
funcEons
and
electron
energies
• Measuring
work
funcEons
• CalculaEng
work
funcEons
• Bulk/surface
contribuEons
• Work
funcEon
engineering
• PracEcal
examples
• CalculaEon
of
surface/interface
energy
in
DFT
• Band
alignment
from
DFT
SURFACES
INTERFACES
50. Work
funcEons
“The
minimum
energy
required
to
remove
an
electron
from
deep
within
the
bulk,
to
a
point
a
macroscopic
distance
outside
the
surface.
”
51. Measuring
work
funcEons
(I)
Ultraviolet
Photoemission
Spectroscopy
(UPS/PES)
hMps://www.tu-‐chemnitz.de/physik/HLPH/elec_spec.htmlhMps://www.tu-‐chemnitz.de/physik/HLPH/elec_spec.html
53. Measuring
Work
funcEons
Just
look
it
up…right?
§
“A single group often obtains different values on
different crystals, different cleaves, or different days”
Surface Science of Metal Oxides: Henrich & Cox
59. Mott-Littleton (1938)
Harwell Labs, UK
A. B. Lidiard, JCSFT 85, 341 (1989)
Daresbury Labs, UK
A. A. Sokol et al, IJCQ 99, 695 (2004)
Limitation: Convergence in region sizes and accurate
analytical MM potentials
Current Implementation:
ChemShell (QM/MM driver)
Bulk Values: An Embedded Crystal
62. Real
capping
layers
PbO2 SiO2 TiO2
Capping
layer
IP
Φ
ΔΦ
(wrt
ITO)
SiO2
11.07
6.87
+0.77
TiO2
10.19
5.99
-‐0.11
PbO2
10.25
6.05
-‐0.05
Phys. Rev. B 89, 115320 (2014)
ITO
replacement
CIGS,
Si
High
Φ
OPV!
63. Content
• Background
and
history
of
surfaces
– History
– Importance
• Important
concepts
for
surface
definiEons
– Energy-‐band-‐alignment
diagram
– Miller
indices
– Tasker
notaEon
– Polar
surfaces
• Surface
energeEcs
and
electronic
structure
– Bond
breaking
approximaEon
– Surface
Tamm
states
• Surfaces
in
PV
– Trapping
– PassivaEon
• Interface
classificaEons
and
formaEon
• Strain
and
supercells
• WeKng
angle
and
cohesion
• Coherent/Semi-‐coherent/Incoherent
• Interfaces
in
PV
• SchoMky
barrier
/
Ohmic
contacts
• Charge
neutrality
level
• Band
Alignment
in
PV
• Work
funcEons
and
electron
energies
• Measuring
work
funcEons
• CalculaEng
work
funcEons
• Bulk/surface
contribuEons
• Work
funcEon
engineering
• PracEcal
examples
• CalculaEon
of
surface/interface
energy
in
DFT
• Band
alignment
from
DFT
SURFACES
INTERFACES
64. PracEcal
Session
• Building
a
good
surface/interface
• CalculaEng
a
surface
energy
• CalculaEng
a
workfuncEon
from
DFT
65. Cut
the
surface
:
METADISE
• Input
unit
cell
and
miller
index
• SystemaEcally
generates
all
cuts
• Checks
for
dipolar
surfaces
66. CalculaEng
a
surface
energy
Calculate
the
energy
of
the
pure
system.
Calculate
the
energy
of
a
2D
slab.
69. Pro-‐Eps
for
surfaces
in
VASP
• k-‐point
sampling
in
the
surface
normal
direcEon
can
be
drasEcally
reduced.
• Vacuums
of
~
15
Angstrom
are
usually
large
enough…check
this
for
convergence
though.
• Slab
thickness
required
varies
–
depends
on
the
system
type.
Generally
–
more
broken
bonds
@
surface
means
more
surface
states
requires
a
thicker
slab
…
eg
layered
systems
are
easy!!
70. Interface
energy
caveat
• SomeEmes
interface
energies
calculated
as
above
converge
very
slowly.
• Calculate
energies
for
several
layer
thicknesses.
71. Pro-‐Eps:
CalculaEng
a
band
alignment
diagram
from
DFT
ICORELEVEL = 1
NEDOS = 1000
NBANDS = 468
1:
Get
the
energy
levels
of
the
bulk
structure
DFT
band
structure
(usually
with
a
hybrid
funcEonal)
Get
energy
difference
between
core
state
and
VBM
hMps://github.com/keeeto/VASPBands
Core
level,
serves
as
a
reference
state
Increase
NEDOS
–
nicer
DOS
plots
Increase
#
bands
quicker
convergence
-‐
NBANDS
=
#
electrons
(spin
unpolarised)
-‐
NBAMDS
=
2x
#electrons
(spin
polarised)
72. Pro-‐Eps:
CalculaEng
a
band
alignment
diagram
from
DFT
ICORELEVEL = 1
LVHAR = .TRUE.
2:
Calculate
the
electrostaEc
potenEal
of
the
slab
structure
Core
level,
serves
as
a
reference
state
Hartree
potenEal
–
converges
more
quickly
than
total
potenEal.
Get
the
VBM
from
core
level
plus
energy
difference
from
the
bulk
calculaEon.
Avoids
surface
state
influence.
73. Pro-‐Eps:
CalculaEng
a
band
alignment
diagram
from
DFT
2:
Calculate
the
electrostaEc
potenEal
of
the
slab
structure
ExtracEng
the
electrostaEc
potenEal
from
LOCPOT
file.
Our
code
MacroDensity
does
this
for
a
range
of
systems
and
electronic
structure
codes.
hMps://github.com/WMD-‐Bath/MacroDensity
input_file = 'LOCPOT.slab'
lattice_vector = 4.75
output_file = 'planar.dat'
# No need to alter anything after
here
#------------------------------------------------------
PlanarAvergae.py
> python PlanarAverage.py
74. Pro-‐Eps:
CalculaEng
a
band
alignment
diagram
from
DFT
2:
Calculate
the
electrostaEc
potenEal
of
the
slab
structure
ExtracEng
the
electrostaEc
potenEal
from
LOCPOT
file.
Our
code
MacroDensity
does
this
for
a
range
of
systems
and
electronic
structure
codes.
hMps://github.com/WMD-‐Bath/MacroDensity
input_file = 'LOCPOT.slab'
lattice_vector = 4.75
output_file = 'planar.dat'
# No need to alter anything after
here
#------------------------------------------------------
PlanarAvergae.py
> python PlanarAverage.py
75. ElectrostaticPotentialPro-‐Eps:
CalculaEng
a
band
alignment
diagram
from
DFT
Bulk
calculaEon
the core state eigenenergies are
1- 1s -87.8177 2s -87.9364 2p -87.9364
2- 1s -87.9771 2s -88.1009 2p -88.1009
76. Important
Points
• Surfaces
consEtute
a
small
part
of
a
system,
but
have
a
huge
influence
on
properEes.
• Energy-‐band-‐diagrams
are
criEcal
for
designing
devices.
• Single
material
calculaEons
can
be
used
to
predict
offsets
in
hetero-‐juncEon
systems…but
cauEon
is
always
advised.
• Both
experimental
and
theoreEcal
characterisaEon
of
surfaces
are
difficult
and
should
be
used
to
compliment
one
another
wherever
possible.