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Current and Future Challenges for TCAD
C. Jungemann and C. Zimmermann
RWTH Aachen University
MOS-AK 2013
Introduction
IntroductionSome examples for current or future devices
Intel TriGate
Kuhn et al., IEDM2012
Tunnel FET Nanorelay (NEMS)
Introduction
Example: 0.5µm SOI NMOSFET
B
G
S Dn+ n+p
0 0.5 1 1.5 2 2.50
10
20
Drain voltage [V]
Dra
incu
rren
t[A
/m]
DDHD
TCAD simulation without II
Difficult to simulate by classical TCAD (DD, HD)
Until recently could not be accurately simulated
Introduction
Example: 0.5µm SOI NMOSFET
B
G
S Dn+ n+p
0 0.5 1 1.5 2 2.50
10
20
Drain voltage [V]
Dra
incu
rren
t[A
/m]
DDHD
TCAD simulation without II
Difficult to simulate by classical TCAD (DD, HD)
Until recently could not be accurately simulated
Introduction
Example: 0.5µm SOI NMOSFET
B
G
S Dn+ n+p
0 0.5 1 1.5 2 2.50
10
20
Drain voltage [V]
Dra
incu
rren
t[A
/m]
DDHD
TCAD simulation without II
Difficult to simulate by classical TCAD (DD, HD)
Until recently could not be accurately simulated
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionTCAD becomes more complex and difficult
I Materials (often multi-dimensional configuration space)I SiGeI III-V, II-VI, CNT, graphene, 2D crystalsI Organic semiconductorsI Metal oxide systems, phase change materials
I PhysicsI Electron transportI Quantum effects (bandstructure, scattering, tunneling, size
quantization etc.)I Multi-physics (mechanics, phonons, electromagnetics, chemicals,
interaction with biological matter etc.)I Device types
I FET, BJTI Tunnel FET, 1D devices, spin torque devices etc.I RRAM, MRAM, PCRAM, NEMS, sensors, bio, ....
I New technologies and materials are often proprietary or kept secret
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionSimulation models
I Quantum transport+ Best physics, close to "first principles"- Limited device size and problem complexity- CPU months, only DC
I Boltzmann equation+ Semiclassical physics with lots of quantum mechanics+ Large devices, coupled problems, DC, AC, noise, (HB or transient)- CPU hours, bulk model required
I Drift-diffusion, hydrodynamic model+ Semiconductor equations and multi-physics+ Very large devices or small circuits, coupled problems, DC, AC,
noise, HB, transient, CPU seconds- Transport parameters, limited accuracy
I Compact models+ CPU milliseconds, large circuits, DC, AC, noise, HB, transient- Analytical equations with device parameters, limited physics
IntroductionConsistent simulation hierarchy
I Quantum transportI Boltzmann equationI Drift-diffusion modelI Compact models
Speed
Acc
urac
y
Com
plexity
AdvantagesI Lower levels are based on approximations of the upper levels
E.g.: DD is based on the first two moments of the BEI Parameter generation for lower levels (table models or analytical
expressions)⇒ ConsistencyE.g.: Mobility for the DD model by BE bulk simulations
I Higher levels require fewer parameters and are easier to match tobasic experiments
I Benchmark simulations by higher levels to assess accuracy ofapproximations (not measurements)
IntroductionConsistent simulation hierarchy
I Quantum transportI Boltzmann equationI Drift-diffusion modelI Compact models
Speed
Acc
urac
y
Com
plexity
AdvantagesI Lower levels are based on approximations of the upper levels
E.g.: DD is based on the first two moments of the BEI Parameter generation for lower levels (table models or analytical
expressions)⇒ ConsistencyE.g.: Mobility for the DD model by BE bulk simulations
I Higher levels require fewer parameters and are easier to match tobasic experiments
I Benchmark simulations by higher levels to assess accuracy ofapproximations (not measurements)
IntroductionConsistent simulation hierarchy
I Quantum transportI Boltzmann equationI Drift-diffusion modelI Compact models
Speed
Acc
urac
y
Com
plexity
AdvantagesI Lower levels are based on approximations of the upper levels
E.g.: DD is based on the first two moments of the BEI Parameter generation for lower levels (table models or analytical
expressions)⇒ ConsistencyE.g.: Mobility for the DD model by BE bulk simulations
I Higher levels require fewer parameters and are easier to match tobasic experiments
I Benchmark simulations by higher levels to assess accuracy ofapproximations (not measurements)
IntroductionConsistent simulation hierarchy
I Quantum transportI Boltzmann equationI Drift-diffusion modelI Compact models
Speed
Acc
urac
y
Com
plexity
AdvantagesI Lower levels are based on approximations of the upper levels
E.g.: DD is based on the first two moments of the BEI Parameter generation for lower levels (table models or analytical
expressions)⇒ ConsistencyE.g.: Mobility for the DD model by BE bulk simulations
I Higher levels require fewer parameters and are easier to match tobasic experiments
I Benchmark simulations by higher levels to assess accuracy ofapproximations (not measurements)
IntroductionConsistent simulation hierarchy
I Quantum transportI Boltzmann equationI Drift-diffusion modelI Compact models
Speed
Acc
urac
y
Com
plexity
AdvantagesI Lower levels are based on approximations of the upper levels
E.g.: DD is based on the first two moments of the BEI Parameter generation for lower levels (table models or analytical
expressions)⇒ ConsistencyE.g.: Mobility for the DD model by BE bulk simulations
I Higher levels require fewer parameters and are easier to match tobasic experiments
I Benchmark simulations by higher levels to assess accuracy ofapproximations (not measurements)
IntroductionConsistent simulation hierarchy
I Quantum transportI Boltzmann equationI Drift-diffusion modelI Compact models
Speed
Acc
urac
y
Com
plexity
AdvantagesI Lower levels are based on approximations of the upper levels
E.g.: DD is based on the first two moments of the BEI Parameter generation for lower levels (table models or analytical
expressions)⇒ ConsistencyE.g.: Mobility for the DD model by BE bulk simulations
I Higher levels require fewer parameters and are easier to match tobasic experiments
I Benchmark simulations by higher levels to assess accuracy ofapproximations (not measurements)
IntroductionConsistent simulation hierarchy
I Quantum transportI Boltzmann equationI Drift-diffusion modelI Compact models
Speed
Acc
urac
y
Com
plexity
AdvantagesI Lower levels are based on approximations of the upper levels
E.g.: DD is based on the first two moments of the BEI Parameter generation for lower levels (table models or analytical
expressions)⇒ ConsistencyE.g.: Mobility for the DD model by BE bulk simulations
I Higher levels require fewer parameters and are easier to match tobasic experiments
I Benchmark simulations by higher levels to assess accuracy ofapproximations (not measurements)
IntroductionConsistent simulation hierarchy
I Quantum transportI Boltzmann equationI Drift-diffusion modelI Compact models
Speed
Acc
urac
y
Com
plexity
AdvantagesI Lower levels are based on approximations of the upper levels
E.g.: DD is based on the first two moments of the BEI Parameter generation for lower levels (table models or analytical
expressions)⇒ ConsistencyE.g.: Mobility for the DD model by BE bulk simulations
I Higher levels require fewer parameters and are easier to match tobasic experiments
I Benchmark simulations by higher levels to assess accuracy ofapproximations (not measurements)
IntroductionConsistent simulation hierarchy
I Quantum transportI Boltzmann equationI Drift-diffusion modelI Compact models
Speed
Acc
urac
y
Com
plexity
AdvantagesI Lower levels are based on approximations of the upper levels
E.g.: DD is based on the first two moments of the BEI Parameter generation for lower levels (table models or analytical
expressions)⇒ ConsistencyE.g.: Mobility for the DD model by BE bulk simulations
I Higher levels require fewer parameters and are easier to match tobasic experiments
I Benchmark simulations by higher levels to assess accuracy ofapproximations (not measurements)
IntroductionConsistent simulation hierarchy
I Quantum transportI Boltzmann equationI Drift-diffusion modelI Compact models
Speed
Acc
urac
y
Com
plexity
AdvantagesI Lower levels are based on approximations of the upper levels
E.g.: DD is based on the first two moments of the BEI Parameter generation for lower levels (table models or analytical
expressions)⇒ ConsistencyE.g.: Mobility for the DD model by BE bulk simulations
I Higher levels require fewer parameters and are easier to match tobasic experiments
I Benchmark simulations by higher levels to assess accuracy ofapproximations (not measurements)
IntroductionConsistent simulation hierarchy
I Quantum transportI Boltzmann equationI Drift-diffusion modelI Compact models
Speed
Acc
urac
y
Com
plexity
AdvantagesI Lower levels are based on approximations of the upper levels
E.g.: DD is based on the first two moments of the BEI Parameter generation for lower levels (table models or analytical
expressions)⇒ ConsistencyE.g.: Mobility for the DD model by BE bulk simulations
I Higher levels require fewer parameters and are easier to match tobasic experiments
I Benchmark simulations by higher levels to assess accuracy ofapproximations (not measurements)
Simulation hierarchy at ITHE for SiGe devices
I Quantum mechanics for band structure calculations (EPM)I Boltzmann equation solvers
I Full-band Monte Carlo simulator (Elwomis), transientI Spherical harmonics expansion solver (SPRING), DC, AC, noise
I Drift-diffusion and hydrodynamic modelsI Galene III (TU BS), table model, DC, AC, noise, transientI Sdevice (Synopsys), limited table model, DC, AC, noise, transient,
HBI Compact models
I HiCum (TUD) with Aperitif
Simulation hierarchy at ITHE for SiGe devices
I Quantum mechanics for band structure calculations (EPM)I Boltzmann equation solvers
I Full-band Monte Carlo simulator (Elwomis), transientI Spherical harmonics expansion solver (SPRING), DC, AC, noise
I Drift-diffusion and hydrodynamic modelsI Galene III (TU BS), table model, DC, AC, noise, transientI Sdevice (Synopsys), limited table model, DC, AC, noise, transient,
HBI Compact models
I HiCum (TUD) with Aperitif
Simulation hierarchy at ITHE for SiGe devices
I Quantum mechanics for band structure calculations (EPM)I Boltzmann equation solvers
I Full-band Monte Carlo simulator (Elwomis), transientI Spherical harmonics expansion solver (SPRING), DC, AC, noise
I Drift-diffusion and hydrodynamic modelsI Galene III (TU BS), table model, DC, AC, noise, transientI Sdevice (Synopsys), limited table model, DC, AC, noise, transient,
HBI Compact models
I HiCum (TUD) with Aperitif
Simulation hierarchy at ITHE for SiGe devices
I Quantum mechanics for band structure calculations (EPM)I Boltzmann equation solvers
I Full-band Monte Carlo simulator (Elwomis), transientI Spherical harmonics expansion solver (SPRING), DC, AC, noise
I Drift-diffusion and hydrodynamic modelsI Galene III (TU BS), table model, DC, AC, noise, transientI Sdevice (Synopsys), limited table model, DC, AC, noise, transient,
HBI Compact models
I HiCum (TUD) with Aperitif
Simulation hierarchy at ITHE for SiGe devices
I Quantum mechanics for band structure calculations (EPM)I Boltzmann equation solvers
I Full-band Monte Carlo simulator (Elwomis), transientI Spherical harmonics expansion solver (SPRING), DC, AC, noise
I Drift-diffusion and hydrodynamic modelsI Galene III (TU BS), table model, DC, AC, noise, transientI Sdevice (Synopsys), limited table model, DC, AC, noise, transient,
HBI Compact models
I HiCum (TUD) with Aperitif
Simulation hierarchy at ITHE for SiGe devices
I Quantum mechanics for band structure calculations (EPM)I Boltzmann equation solvers
I Full-band Monte Carlo simulator (Elwomis), transientI Spherical harmonics expansion solver (SPRING), DC, AC, noise
I Drift-diffusion and hydrodynamic modelsI Galene III (TU BS), table model, DC, AC, noise, transientI Sdevice (Synopsys), limited table model, DC, AC, noise, transient,
HBI Compact models
I HiCum (TUD) with Aperitif
Simulation hierarchy at ITHE for SiGe devices
I Quantum mechanics for band structure calculations (EPM)I Boltzmann equation solvers
I Full-band Monte Carlo simulator (Elwomis), transientI Spherical harmonics expansion solver (SPRING), DC, AC, noise
I Drift-diffusion and hydrodynamic modelsI Galene III (TU BS), table model, DC, AC, noise, transientI Sdevice (Synopsys), limited table model, DC, AC, noise, transient,
HBI Compact models
I HiCum (TUD) with Aperitif
Simulation hierarchy at ITHE for SiGe devices
I Quantum mechanics for band structure calculations (EPM)I Boltzmann equation solvers
I Full-band Monte Carlo simulator (Elwomis), transientI Spherical harmonics expansion solver (SPRING), DC, AC, noise
I Drift-diffusion and hydrodynamic modelsI Galene III (TU BS), table model, DC, AC, noise, transientI Sdevice (Synopsys), limited table model, DC, AC, noise, transient,
HBI Compact models
I HiCum (TUD) with Aperitif
Simulation hierarchy at ITHE for SiGe devices
I Quantum mechanics for band structure calculations (EPM)I Boltzmann equation solvers
I Full-band Monte Carlo simulator (Elwomis), transientI Spherical harmonics expansion solver (SPRING), DC, AC, noise
I Drift-diffusion and hydrodynamic modelsI Galene III (TU BS), table model, DC, AC, noise, transientI Sdevice (Synopsys), limited table model, DC, AC, noise, transient,
HBI Compact models
I HiCum (TUD) with Aperitif
Simulation hierarchy at ITHE for SiGe devices
I Quantum mechanics for band structure calculations (EPM)I Boltzmann equation solvers
I Full-band Monte Carlo simulator (Elwomis), transientI Spherical harmonics expansion solver (SPRING), DC, AC, noise
I Drift-diffusion and hydrodynamic modelsI Galene III (TU BS), table model, DC, AC, noise, transientI Sdevice (Synopsys), limited table model, DC, AC, noise, transient,
HBI Compact models
I HiCum (TUD) with Aperitif
First example: THz npn SiGe HBT
2D THz SiGe HBT
2D Schematic
I Symmetric structureI Emitter width = 50nmI Spacer = 25nmI Selectively implanted
collector (SIC)
I 148 by 23 grid points
2D THz SiGe HBT
2D Schematic
I Symmetric structureI Emitter width = 50nmI Spacer = 25nmI Selectively implanted
collector (SIC)
I 148 by 23 grid points
2D THz SiGe HBT
1D doping and Ge profiles
I Base thick. = 7nmI Box Ge = 18%
I 5meV, 3rd orderI Galene III for DD/HDI Boltzmann statisticsI No recombinationI No self-heating
2D THz SiGe HBT
1D doping and Ge profiles
I Base thick. = 7nmI Box Ge = 18%
I 5meV, 3rd orderI Galene III for DD/HDI Boltzmann statisticsI No recombinationI No self-heating
2D THz SiGe HBTVCB = 0.1V
Log scale Linear scale
For VBE larger than 0.9V overestimation by DD/HD models
2D THz SiGe HBTVCE = 1.0V
Cutoff frequency
100 101 102
Collector current [mA/µm2]
0.0000
200.00
400.00
600.00
800.00
1000.0
1200.0
Cut
off f
requ
ency
[G
Hz]
DDHDBE
Drift velocity
DD and HD model fail!
2D THz SiGe HBTVCB = 0.1V , BE results
Transit time distribution Extrinsic contributions
Emitter dominates the transit time! Why?
2D THz SiGe HBTVCB = 0.1V
Box and drift Ge profiles
.010 .020 .030 .040
x [µm]
1017
1018
1019
1020
1021
Dopin
g [c
m-3
]
ND
NA
.010 .020 .030 .040
x [µm]
0.00
5.00
10.0
15.0
20.0
25.0
30.0
Ge c
onte
nt [%
]
Drift Ge
Box Ge
Cutoff frequency
101
102
Collector current [mA/µm2]
103
5*102
Cuto
ff fre
quency [G
Hz]
Drift(SHE)
Box(SHE)
Drift(DD)
Box(DD)
Improvement due to bandstructure effects, not drift field!Bandstructure effects not captured by DD or HDOptimization involved three levels of the hierarchy: BE (Spring),TCAD (DD, HD), and compact modeling (HiCum)
2D THz SiGe HBTVCB = 0.1V
Box and drift Ge profiles
.010 .020 .030 .040
x [µm]
1017
1018
1019
1020
1021
Dopin
g [c
m-3
]
ND
NA
.010 .020 .030 .040
x [µm]
0.00
5.00
10.0
15.0
20.0
25.0
30.0
Ge c
onte
nt [%
]
Drift Ge
Box Ge
Cutoff frequency
101
102
Collector current [mA/µm2]
103
5*102
Cuto
ff fre
quency [G
Hz]
Drift(SHE)
Box(SHE)
Drift(DD)
Box(DD)
Improvement due to bandstructure effects, not drift field!Bandstructure effects not captured by DD or HDOptimization involved three levels of the hierarchy: BE (Spring),TCAD (DD, HD), and compact modeling (HiCum)
2D THz SiGe HBTVCB = 0.1V
Box and drift Ge profiles
.010 .020 .030 .040
x [µm]
1017
1018
1019
1020
1021
Dopin
g [c
m-3
]
ND
NA
.010 .020 .030 .040
x [µm]
0.00
5.00
10.0
15.0
20.0
25.0
30.0
Ge c
onte
nt [%
]
Drift Ge
Box Ge
Cutoff frequency
101
102
Collector current [mA/µm2]
103
5*102
Cuto
ff fre
quency [G
Hz]
Drift(SHE)
Box(SHE)
Drift(DD)
Box(DD)
Improvement due to bandstructure effects, not drift field!Bandstructure effects not captured by DD or HDOptimization involved three levels of the hierarchy: BE (Spring),TCAD (DD, HD), and compact modeling (HiCum)
2D THz SiGe HBTVCB = 0.1V
Box and drift Ge profiles
.010 .020 .030 .040
x [µm]
1017
1018
1019
1020
1021
Dopin
g [c
m-3
]
ND
NA
.010 .020 .030 .040
x [µm]
0.00
5.00
10.0
15.0
20.0
25.0
30.0
Ge c
onte
nt [%
]
Drift Ge
Box Ge
Cutoff frequency
101
102
Collector current [mA/µm2]
103
5*102
Cuto
ff fre
quency [G
Hz]
Drift(SHE)
Box(SHE)
Drift(DD)
Box(DD)
Improvement due to bandstructure effects, not drift field!Bandstructure effects not captured by DD or HDOptimization involved three levels of the hierarchy: BE (Spring),TCAD (DD, HD), and compact modeling (HiCum)
2D THz SiGe HBTVBE = 0.84V
Output characteristics with impact ionizationfT ∗ BVCE0 ≈ 1100GHzV
2D THz SiGe HBTVCB = 0.1V
VCB = 0.1V , 100GHz VBE = 0.7V , VCB = 0.1V
Noise characterization
2D THz SiGe HBT
I (At least) three ordersof magnitude slowerthan DD/HD model
I Dependent on SHEorder
I Dependent on the biasI Dependent on the initial
potential
OLED
Schematic Structure of an OLED Device and theOrganic Stack
I HTL: hole transport layerI EL: emission layerI ETL: electron transport layerI LUMO: lowest unoccupied molecule orbitalI HOMO: highest occupied molecule orbital
Composition of Efficient OLED Stacks
N N
N N
N
Ir
N
N
N
N
N N
some common organic materials
I huge number of organic materials with specific functionalityavailable with largely unknown parameters
I complex stacks necessary for efficient devicesI small variations in device structure considerably affect color point,
efficiency, life time
Composition of Efficient OLED Stacks
N N
N N
N
Ir
N
N
N
N
N N
some common organic materials
I huge number of organic materials with specific functionalityavailable with largely unknown parameters
I complex stacks necessary for efficient devicesI small variations in device structure considerably affect color point,
efficiency, life time
Composition of Efficient OLED Stacks
N N
N N
N
Ir
N
N
N
N
N N
some common organic materials
I huge number of organic materials with specific functionalityavailable with largely unknown parameters
I complex stacks necessary for efficient devicesI small variations in device structure considerably affect color point,
efficiency, life time
Composition of Efficient OLED Stacks
N N
N N
N
Ir
N
N
N
N
N N
some common organic materials
I huge number of organic materials with specific functionalityavailable with largely unknown parameters
I complex stacks necessary for efficient devicesI small variations in device structure considerably affect color point,
efficiency, life time
Composition of Efficient OLED Stacks
N N
N N
N
Ir
N
N
N
N
N N
some common organic materials
I huge number of organic materials with specific functionalityavailable with largely unknown parameters
I complex stacks necessary for efficient devicesI small variations in device structure considerably affect color point,
efficiency, life time
Characteristics of Carrier Transport
Very strong mobility dependence on temperature, field and carrierconcentration
Challenges for Simulation
Model uncertainties:I transition rates: thermally activated tunnelling, non-adiabatic small
polarons, ...I energetic disorder: deviations from Gausian density of states,
correlated or uncorrelated disorderI spatial/configurational disorder: strength and mathematical
description unknownI additional material-specific effects: e.g. trapsI interaction between different organic molecule typesI dipole formation at electrode-organic and organic-organic
interfacesI influence of deposition parameters
Challenges for Simulation
Model uncertainties:I transition rates: thermally activated tunnelling, non-adiabatic small
polarons, ...I energetic disorder: deviations from Gausian density of states,
correlated or uncorrelated disorderI spatial/configurational disorder: strength and mathematical
description unknownI additional material-specific effects: e.g. trapsI interaction between different organic molecule typesI dipole formation at electrode-organic and organic-organic
interfacesI influence of deposition parameters
Challenges for Simulation
Model uncertainties:I transition rates: thermally activated tunnelling, non-adiabatic small
polarons, ...I energetic disorder: deviations from Gausian density of states,
correlated or uncorrelated disorderI spatial/configurational disorder: strength and mathematical
description unknownI additional material-specific effects: e.g. trapsI interaction between different organic molecule typesI dipole formation at electrode-organic and organic-organic
interfacesI influence of deposition parameters
Challenges for Simulation
Model uncertainties:I transition rates: thermally activated tunnelling, non-adiabatic small
polarons, ...I energetic disorder: deviations from Gausian density of states,
correlated or uncorrelated disorderI spatial/configurational disorder: strength and mathematical
description unknownI additional material-specific effects: e.g. trapsI interaction between different organic molecule typesI dipole formation at electrode-organic and organic-organic
interfacesI influence of deposition parameters
Challenges for Simulation
Model uncertainties:I transition rates: thermally activated tunnelling, non-adiabatic small
polarons, ...I energetic disorder: deviations from Gausian density of states,
correlated or uncorrelated disorderI spatial/configurational disorder: strength and mathematical
description unknownI additional material-specific effects: e.g. trapsI interaction between different organic molecule typesI dipole formation at electrode-organic and organic-organic
interfacesI influence of deposition parameters
Challenges for Simulation
Model uncertainties:I transition rates: thermally activated tunnelling, non-adiabatic small
polarons, ...I energetic disorder: deviations from Gausian density of states,
correlated or uncorrelated disorderI spatial/configurational disorder: strength and mathematical
description unknownI additional material-specific effects: e.g. trapsI interaction between different organic molecule typesI dipole formation at electrode-organic and organic-organic
interfacesI influence of deposition parameters
Challenges for Simulation
Model uncertainties:I transition rates: thermally activated tunnelling, non-adiabatic small
polarons, ...I energetic disorder: deviations from Gausian density of states,
correlated or uncorrelated disorderI spatial/configurational disorder: strength and mathematical
description unknownI additional material-specific effects: e.g. trapsI interaction between different organic molecule typesI dipole formation at electrode-organic and organic-organic
interfacesI influence of deposition parameters
Challenges for Simulation
Model uncertainties:I transition rates: thermally activated tunnelling, non-adiabatic small
polarons, ...I energetic disorder: deviations from Gausian density of states,
correlated or uncorrelated disorderI spatial/configurational disorder: strength and mathematical
description unknownI additional material-specific effects: e.g. trapsI interaction between different organic molecule typesI dipole formation at electrode-organic and organic-organic
interfacesI influence of deposition parameters
Challenges for Simulation
Model uncertainties:I transition rates: thermally activated tunnelling, non-adiabatic small
polarons, ...I energetic disorder: deviations from Gausian density of states,
correlated or uncorrelated disorderI spatial/configurational disorder: strength and mathematical
description unknownI additional material-specific effects: e.g. trapsI interaction between different organic molecule typesI dipole formation at electrode-organic and organic-organic
interfacesI influence of deposition parameters
Conclusions
ConclusionsI The full hierarchy of simulation tools is requiredI Hierarchy should be consistentI TCAD becomes more and more complex (e.g. OLED)I Too many choices, TCAD development lags behindI Slow flow of information hinders development
ConclusionsI The full hierarchy of simulation tools is requiredI Hierarchy should be consistentI TCAD becomes more and more complex (e.g. OLED)I Too many choices, TCAD development lags behindI Slow flow of information hinders development
ConclusionsI The full hierarchy of simulation tools is requiredI Hierarchy should be consistentI TCAD becomes more and more complex (e.g. OLED)I Too many choices, TCAD development lags behindI Slow flow of information hinders development
ConclusionsI The full hierarchy of simulation tools is requiredI Hierarchy should be consistentI TCAD becomes more and more complex (e.g. OLED)I Too many choices, TCAD development lags behindI Slow flow of information hinders development
ConclusionsI The full hierarchy of simulation tools is requiredI Hierarchy should be consistentI TCAD becomes more and more complex (e.g. OLED)I Too many choices, TCAD development lags behindI Slow flow of information hinders development
ConclusionsI The full hierarchy of simulation tools is requiredI Hierarchy should be consistentI TCAD becomes more and more complex (e.g. OLED)I Too many choices, TCAD development lags behindI Slow flow of information hinders development
-0.30 -0.20 -0.10 0.000 0.10 0.20 0.30
0.00
0.05
0.10
0.15
0.20
0.25
Silicon
Bottom oxide
GS D
Top oxide
Partially depleted SOI NMOSFET
PDSOI NMOSFET
0 0.2 0.4 0.6 0.8 10
5
10
15
20
25
Drain voltage [V]
Dra
incu
rren
t[A
/m]
with IIw/o II
Kink effect due to impact ionization (II) (Vgate = 1.0V )CPU time: 5h per bias point
PDSOI NMOSFET
0 0.2 0.4 0.6 0.8 10
5
10
15
20
25
Drain voltage [V]
Dra
incu
rren
t[A
/m]
with IIw/o II
Kink effect due to impact ionization (II) (Vgate = 1.0V )CPU time: 5h per bias point
PDSOI NMOSFET
0 0.2 0.4 0.6 0.8 10
5
10
15
20
25
Drain voltage [V]
Cur
rent
[A/m
]ID with IIID w/o II
10−16
10−15
10−14
10−13
10−12
10−11
10−10
II-curr.
About 17 orders of magnitude difference in currents at kink
PDSOI NMOSFET
Ele
ctro
n d
ensi
ty [
/cm
3]
1.0×107
1.0×108
1.0×109
1.0×1010
1.0×1011
1.0×1012
1.0×1013
1.0×1014
1.0×1015
1.0×1016
1.0×1017
1.0×1018
1.0×1019
1.0×1020
Vertical p
osition [u
m]
0
0.1
0.18
Lateral position [um]−0.3 −0.2 −0.1 0 0.1 0.2 0.3
SourceDrain
No problems with stability! (Vgate = Vdrain = 1V )
PDSOI NMOSFET
0.00 .050 0.10 0.15 0.20 0.25 0.30
Lateral position [µm]
200.00
400.00
600.00
800.00
1000.0
1200.0
1400.0
1600.0
Dyn
am
ic t
em
pe
ratu
re
[K]
dyn. temp.
0.00 .050 0.10 0.15 0.20 0.25 0.30
Lateral position [µm]
1011
1013
1015
1017
1019
II g
en
era
tio
n r
ate
[c
m-3
s-1
]
II rate
No spurious particle heating! (Vgate = Vdrain = 1V )
PDSOI NMOSFET
10−1 100 101 102 103 104 105 106
10−22
10−19
10−16
10−13
10−10
Frequency [Hz]
Dra
incu
rren
tnoi
se[A
2 s/c
m] Total
HolesElec.
II
Noise can be calculated for individual sources (Vgate = Vdrain = 1V )
top related