Simulation tools in advanced chalcogenide PV
Carmen M. Ruiz-Herrero,
Université Aix-Marseille, France
• Why we need modeling on PV
• Developing a physical model
• 1D simulations
• 2D and 3D simulations• 2D and 3D simulations
• Hybrid models
• Conclusions
Why we need modeling on PV
PV are complex systems:
Different materials
Optical and electrical problems
Technological limitations
Difficult to understand what it is happening
Modeling allows:
A more analytical aproach to the problem
Better design of experiments
Optimization of parameters
Better control of certain situations ( diffusion , second phase generation…)
Easy validation of diagnostics
Developing a physical model
A model has to be realistic
Mathematics and computers allows anything
Need of materials characterization:
- Optical
-- Electrical
Clarify the cell structure:
- Presence of layers
- secondary phases
- gradients- gradients
Simulation tools:-Dimensions
- Equations and algorithms
- electrical vs optical potential
- limitations
Developing a physical model Material parameters
Some basical parameters :
Bandgap
Optical absorption
Doping density
Thickness
Mobility
Not so basical parameters :
Electronic affinity
Dielectric permittivity
Defect structure
Carrier lifetime
Developing a physical model Clarify the cell structure
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
-20
-10
0
jtot(
mA
/cm
2)
v(V)
Importance of a good model: difference between ideal and realistic
-40
-30
-20
Modelled valuesVoc = 0.6918 VoltJsc = 35.74 mA/cm2FF = 81.03 %eta = 20.0 %
Voc = 0.691953 VoltJsc = 35.72509443 mA/cm2FF = 79.2841 %eta = 19.5991 %
jtot(
mA
/cm
2)Experimental values
Developing a physical model Simulation tools
1D vs multidimensional
- Speed
- Easy definition
- Variation of definitions
and parameters
1D Multidimensional
- Structural complexity
- Second order optical and
electrical effects
- Powerfull analysisand parameters
- Most codes are freeware- Powerfull analysis
-Effects such as parallel resistance or
optical interference not taken into account
-Rugosity, grain boundaries
- Not homogeneus distribution of phases
-Time and resource consuming
- Codes are expensive
- Dificult definition files
1D simulations
Software choice
PC1D: first developed 1 dimensional code. Very good for thick cells and silicon
AMPS: open source. WX-AMPS specially developed for thin films
AFORS-HET: conceived for amorphous silicon solar cells
SCAPS-1D: Initially developed for CdTe, very powerful for CIGS systems,
potential for kesteritas
All of them are easy to run, freeware and with a big community of users.
Example: NREL 2009 CIGS record
0
10
20
30
40
50
Ga
cont
ent (
at%
)
1,0
1,2
1,4
Eg(
Ev)
NREL CIGS recordNREL CIGS record Reproduction Reproduction withwith SCAPSSCAPS
0,0 0,5 1,0 1,5 2,00
Absorber thickness from the back contact (µm)
1,0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
-40
-30
-20
-10
0
Modelled valuesVoc = 0.6918 VoltJsc = 35.74 mA/cm2FF = 81.03 %eta = 20.0 %
Voc = 0.691953 VoltJsc = 35.72509443 mA/cm2FF = 79.2841 %eta = 19.5991 %
jtot(
mA
/cm
2)
v(V)
Experimental values
Example: diagnostics of doping problems
V(mV) →Small differences in J
VVococ(mV)(mV) JJscsc(mA/cm(mA/cm22)) FF(%)FF(%) Eff(%)Eff(%)
Standard cellStandard cell 816 21.261 75.17 13.04
Bad CISBad CIS 632 20.788 55.14 7.24
Bad MoSBad MoS22 633 21.378 64.06 8.66
Bad Bad CdSCdS 776 21.485 69.88 11.65
Bad CISBad CIS--ββββββββ 713 22.164 63.31 10.01
SCAPS
0 200 400 600 800
-25
-20
-15
-10
-5
0
J sc(m
A/c
m2 )
V(mV)
Standard cell Bad CIS layer Bad MoS
2 layer
Bad CdS layer Bad CIS-ββββ
→Small differences in Jsc
→ When the problem is on the back of the device,
there is an increase of the Voc losses
→ Problems on the CIS doping level lead to the
bigger losses (up to22.5%)
→ The barrier generated by the MoS2 layer
generates losses of 22.4%
→ The CIS-β layer presents the smallest losses onVoc (12.6%), but FF is severely affected
→ The p-i-n structure generated by an isolant CdS
does not affect very mcuh to Voc
C.M. Ruiz et V. Bermúdez (2009) Proceedings of the 34th IEEE Photovoltaics Specialist Conference
Example: implementing defect levels
Selenium vacancies
Donor at 110 meV detected by Admittance Spectroscopy
-0.2 0.0 0.2 0.4 0.60
5
10
15
20
25
30
35
J(m
A/c
m2 )
Experimental values Fitted curve
1E18
DOS Error
DO
S (
1/cm
3 eV)
-0.2 0.0 0.2 0.4 0.6
-5V(V)
-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6
-10
0
10
20
30
jtot(
mA
/cm
2)
v(V)
Medium density High density
Voc Losses : increase of resistance in the layer
Jsc loses: charge trapping in the SCR
Loss of efficiency
0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40
1E17
E (eV)
Multidimensional simulations� Finite Element methode
� 1D, 2D and 3D simulations
12
_ZnO
Cds
� Space (Δx, Δy, Δz) discretization
� Grain structure
� Rugosity
�Grain boundaries+
CZTS
Mo
Glass
�Steady state resolution of the coupled
Poisson’s and continuity equations
� Newton and Gummel iterations
[1,2]
[1] H. K. Gummel, IEEE Trans. Electron Devices 11, 455 1964.
[2] D. L. Scharfetter and H. K. Gummel, IEEE Trans. Electron Devices, 16, 64 1969
�Grain boundaries
�Secondary phases
Hybrid modelUsing SPICE+ 1 dimensional curves A multidimensional electrical model
CIGS and kesterites can not simply be simulated by a diode:
we extract IV curves from 1D softwares and generate a « paralel matrix » of curves
It allows an interesting aproach to problems such as front contact design, grain boundaries
or cell interconnection
Hybrid modelImpact of lateral resistances in ZnO and Mo:
From 1D simulation to a 1000 cells matrixImpact of front grid design:
100x100 cell matrix
Impact of grain boundary capacitance in a 20x20 cell matrix
WHAT WE CAN AND CANNOT EXPECT FROM MODELING
� Qualitative information about impacts of variations on an standard structure
� Better understanding of electrical and optical phenomena involved
� Rapid testing of parameters variation on a provided and reliable model
� Information proportional to the quality of the model used
� Absolute quantitative information
� Insights in process steps for cell optimization
� Actual device behavior explanations
� Crackpot intelligence with basic parameters
� Actual results without a reliable model