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The Role of Simulation in Photovoltaics:
From Solar Cells To Arrays
Ricardo Borges, Kurt Mueller, and
Nelson Braga
Synopsys, Inc.
1
PV System Challenges
• Improving PV efficiency
• Optimizing for design performance and target reliability
• Reducing the effects of variation on system performance
• Predicting manufacturing yields
• Lowering production costs
2
Addressing Issues at All Stages
Cell Module System
Synopsys TCAD tools Synopsys Saber tools
Design criteria – Cell Level
• Maximize efficiency
• Optimize geometric and process parameters
Design criteria – Module Level
• Minimize effect of interconnects on performance
• Minimize impact of cell variation or degradation on module performance
Design Criteria – System Level
• Maximize system performance accounting for diurnal solar inclination and tracking of solar
path (some systems have 1- or 2-axis tracking of the sun)
• Maximize system level efficiency delivered to the grid, including inverter system
3
What is TCAD?
Process Simulation Device Simulation
Current in Drift-Diffusion Model
PDE for Pair Diffusion
Potential distribution in flash memory
LDMOS: doping, mesh
1D doping profile simulation
Inductance Simulation
PVD (Physical Vapor Deposition)
Snapback of a UMOS
Photogeneration in CIS
Full Chip H-Bridge EM Wave
Mechanical stress in intermetal dielectric AlGaAs VCSEL
4
Why Simulate Solar Cells?
• Continuous innovation makes cells more complex
– More process and geometrical variables
– 3D effects, complex light path, etc …
• It’s impractical to design new cells without simulation
– Too many experiments are needed to investigate design space
– Risks missing optimum design and market window
Early generation cell (Eff ~ 15-16%) New generation cell (Eff ~ 20%)
Source: SERIS
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Solar Cell Simulation Flow
Input Simulation Output
Process Flow Recipe Process Device Geometry
Simulation (doping profiles)
Device Geometry External reflection
Optical
Simulation
Optical Data: n & k Optical generation
Electrical Data: SRH, IQE, EQE
Auger, BGN, Mobility Electrical
Simulation
Device Geometry
(lifetime, doping profiles) Dark & Light I-V
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Example: 2D Cell Optimization
• Select parameters to be investigated
• Parameterize the TCAD model
• Run simulations
• Visualize the influence of each parameter
Wfront Sf
dlfsf
Nlfsf
Nbulk
dsub tbulk
dbsf Sb Nlbsf
dlbsf
wback
wtot
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Example: Unit Cell Optimization Results
wfront wback wtot dsub Nbulk dbsf Nlbsf dlbsf Nlfsf dlfsf tbulk Sf Sb
eff
FF
Voc
jsc
• Each array of points represents a separate simulated condition
• Unit cell pitch, base layer thickness, doping, and lifetime, and
surface recombination velocity show major influence on cell
response
• Design trade-offs can be investigated quantitatively
8
Application: Back-contact Silicon Cells
• Design problem: optimization of metal finger pitch to achieve good
performance with low cost screen printing manufacturing
• Simulation correctly captures the measured behavior across a range of
contact pitch and bulk resistivity
• Optimization of the structure results in 21.3% efficiency
Surface Texturing Antireflection
Coating (ARC)
n+ FSF
Base (n-type)
Passivation
n+ BSF p+ emitter Metal Contacts
Pitch
Source: F. Granek et al, Progress in Photovoltaics: Research and Applications, 17, Oct 2008, pp 47-56
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Application: Multi-Junction Solar Cells
• GaAs/GaInP Dual-Junction Cell
• Excellent match between Sentaurus simulation and measurements in
MJ cells
• Calibrated model allows researchers to explore more advanced
structures: Bragg reflectors, additional junctions, etc
Source: Philipps, S.S. et.al.. NUMERICAL SIMULATION AND MODELING OF III-V MULTI-JUNCTION SOLAR CELLS Proceedings of
23rd EUPVSEC, 2008
10
Cells to Systems: Why simulate?
• Cells alone are physically interesting;
• Modules and Systems bring the power of the sun to
the end user
• Once cell behavior is understood, need model
capable of system-level simulation to:
– Minimize interconnect losses
– Evaluate effects of environmental variation:
• Light intensity and incidence angle
• Temperature variation
• Electrical environment
• Optimize power conversion
11
What is Saber?
Multi-domain circuit simulation… enabling full system “Virtual Prototyping”
Power Electronics
Multiple Domains
Behavioral Models
Control Algorithms
Nominal Parameter Production Statistical Fault Worst-
Design Optimizing System Performance
Variation Tolerances Analyses Reliability
andAnalyses Case
12
Cells to Modules
• Design problem: active width optimization
• Given TCAD device design, physical parameters contributing to
interconnect resistances can be extracted and a system-level model
developed
13
Module Optimization
Module Optimization: Variation of equivalent RSeries & RShunt
• From system cell level
model, sweeps can be
done to determine the
V
effect of different cell
widths on module
performance
• Allows for optimization
of Maximum Power
Point at a module
V
level as a function of
luminance and cell
width
IModule (A)
14
Module Validation
• Accurate, physics-based models take TCAD results to system
simulation for validating real-world measurements
15
Modules to Arrays and Systems
Photovoltaic Module Performance Verification at Different Cell Temperatures
Measurement of MPPT at Different Temperatures
• Design problem:
Thermal Effects
on Module/Array
performance and
Maximum Power
Point
• Analysis of faults
on strings within
the array
16
System Integration & Optimization
• Simulation provides integrated test, validation and optimization
environment for all aspects of the system:
Environment
Power Electronics
Control System &
Algorithms
17
Battery Charging System Simulation
System highlights:
• Maximum Power Point Tracking through impedance matching using
controlled DC/DC converter
• Dynamic thermal capable array model
18
Unit Cells to Systems Simulation
• Early validation of novel cell design
• Development of application-optimized
cells, modules and arrays
• System level virtual prototyping for test &
validation before anything physical is built
19
Predictable Success
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