Design, Modeling, and Optimization
of Silicon Solar Cells and Modules
Victor Moroz
© Synopsys 2011
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Outlook
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Outlook
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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
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Addressing Issues at All Stages
Cell
Module
Synopsys TCAD tools
System
Synopsys Saber tools
Design criteria – Cell Level
• Maximize efficiency
• Optimize cell: contact pitches, junctions, anti-reflective coatings, etc.
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
• Maximize system level efficiency delivered to the grid, including inverter system
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Why Simulate Solar Cells?
Source: SERIS
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Early generation cell (Eff ~ 17%)
6
New generation cell (Eff > 20%)
Solar Cell Simulation Flow
Input
Simulation
Process Flow Recipe
Process
Simulation
Texture
Optical Data: n & k
Electrical Data: SRH,
Auger, BGN, Mobility
Device Geometry
(lifetime, doping profiles)
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Optical
Simulation
Electrical
Simulation
Output
Texture
Junctions
External reflection
Optical generation
IQE, EQE
Dark & Light I-V
Outlook
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Solar PV Driving Force : $/W
R&D cost ~ 1.5%
(about 1.2GW shipment)
(thousands)
Net revenues
$2,000,000
Cost of revenues
$1,320,000 66.0%
Gross profit
$680,000 34.0%
Operating expenses
Cost
=
Watt
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Production cost ($)
Output power (W)
~
SG&A
$160,000 8.0%
R&D
$30,000 1.5%
Total operating expenses
$190,000 9.5%
Operating income
$490,000 24.5%
Process Cost
Conversion efficiency
Efficiency vs Profit
26%
p-type c-Si
Conversion Efficiency (E)
Lab
24%
• Impact of DE = 0.5%
22%
– E0=20% -> E=20.5%
– DE/E0= 2.5%
20%
Production
18%
Pilot
16%
• 2.5% more power output
• 2.4% less cost per Watt
• 2.4% more gross profit
14%
2008 2009 2010 2011 2012 2013 2014 2015
• 2.4% more gross profit = $1.32B * 2.4% = $32M1
• About the same as R&D budget ($30M )
1Assume
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the same cost per unit module and wattage sales
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Outlook
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Measured Texture
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Source: AMAT
Simulated Surface Texture
Zoom-in
20um * 20um surface
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Behavior of UV light (0.3um Wavelength)
Bounced
out ray
Incoming ray
Absorbed
ray
12um
•
•
•
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Absorption in Si
happens within one
micron from surface
Typically one or two
reflection events
Only top surface
matters
Behavior of Visible Light (l=0.6um)
Bounced
out ray
(reflectance)
Incoming ray
Absorbed
rays
12um
•
•
Bounced
out rays
(transmittance)
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Absorption in Si
happens within tens
of microns
Several reflection
events
Behavior of Infrared Light (l=0.9um)
Bounced
out rays
Incoming ray
Absorbed
rays
12um
•
•
Bounced
out rays
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•
Absorption in Si
happens within
hundreds of microns
Dozens of reflection
events
Both the top and the
rear surfaces matter
Reflectance Curves: Texture is Good
90.00
80.00
flat/flat
textured/flat
textured/textured
Reflectance, %
70.00
60.00
50.00
bad
40.00
30.00
good
20.00
10.00
0.00
0.3
0.4
0.5
0.6
0.7
0.8
Wavelength, um
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0.9
1.0
1.1
1.2
Reflectance Curves: Nitride is Good
60.00
Reflectance, %
50.00
textured/textured
textured+nitride/textured
40.00
30.00
20.00
10.00
Ideal!
0.00
0.3
0.4
0.5
0.6
0.7
0.8
Wavelength, um
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0.9
1.0
1.1
1.2
Model Accuracy
60
Textured Si, measured data
50
Textured+SiN, measured data
Reflectance, %
Textured Si, model
40
Textured+SiN, model
•
30
20
10
0
0.95
1
1.05
1.1
1.15
1.2
Wavelength, um
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Si data from AMAT
Calibrated model
captures Si texture and
nitride ARC film
Outlook
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c-Si Solar Cell with Rear Point Contacts
Rear point contacts (Al):
Si-Al interface
Front contact stripe
(silver)
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Rear surface not covered by
contact: Si-Nitride interface
Rear Point Contact Optimization
Doping
Silicon quality
Cell size
Contact pitch
…
Increasing Rear Point
Contact Area is GOOD!
Current Crowding
Contact Resistance
Bulk Recombination
Rear Point
Contact
Design
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Increasing Rear Point
Contact Area is BAD!
Optical Reflectivity
Surface Recombination Rates
Junction Optimization
Junction depth
…
Higher doping in n layer
is BAD!
Higher doping increases
recombination
Higher doping in n layer
is GOOD!
Higher doping increases
conduction
Top Surface
p-n Junction
Design
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Modeling Major Effects
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•
•
•
•
Optical Reflectivity
Surface Recombination
Contact Resistance
Bulk Recombination
Current Crowding
Shaded area
under top contact
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Current Crowding Pattern
Current crowding
is observed in both
lateral directions,
which makes it a
3D effect
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Rear Contact Optimization
Best design with > 1% efficiency advantage
solar cell design
with full backside
coverage
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Junction Optimization
Resistance
22.0
Recombination
Cell Efficiency (%)
21.0
20.0
Xj=0.1um
Xj=0.2um
Xj=0.4um
Xj=0.6um
Xj=0.8um
Xj=1.0um
19.0
18.0
17.0
16.0
1.E+18
Shallow
junction
Deep
junction
1.E+19
1.E+20
Peak Doping Concentration (cm-3)
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1.E+21
Junction Optimization
Improved
junctions
22.0
Cell Efficiency (%)
21.0
20.0
19.0
Industry
standard
POCl
junctions
Xj=0.1um
Xj=0.4um
18.0
Xj=0.6um
17.0
Xj=0.8um
16.0
1.E+18
1.E+19
1.E+20
Peak Doping Concentration (cm-3)
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1.E+21
Outlook
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System Integration & Optimization
•
Simulation provides integrated test, validation and optimization
environment for all aspects of the system:
Environment
Power Electronics
Control System &
Algorithms
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Modules to Arrays and Systems
• Design problem:
Thermal Effects
on Module/Array
performance and
Maximum Power
Point
• Analysis of faults
on strings within
the array
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Photovoltaic Module Performance Verification at Different Cell Temperatures
Measurement of MPPT at Different Temperatures
Summary
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