current and future challenges for tcad -...

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 )