The Potential Role of

Modeling and

Simulation in PV Industry

BAPVC Workshop and Industry Board Meeting, January 12, 2012

Muhammad Alam and Mark Lundstrom

Birck Nanotechnology Center

Purdue University

Simulation in PV Industry

1

Outline

Historical role of Modeling and Simulation in PV industry

Opportunities to contribute to end-to-end modelingExample 1: Organic Solar CellsExample 2: Shunt Conduction

How to connect industry and academia by nanohub--- Simulation tools --- Tutorials and educational material

Conclusions

Thermodynamic Limit of Efficiency

0 ( ,) 0, )( phS SEg SI D E n dq E c ETµ∞

× ∆= =×Ω ×∫

( )0outSQ

i n

t

n i

spoVP II

P Pη

−= =

(( , , ))sp D DEg ph Dq D EI qVE T dcn Eµ−∆

∞Ω ∆ =××= ∫

W. Shockley, H. Queisser,JAP, 32(3), 510, 1961.

30.5 1 1.5 2 2.5 30

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Effi

cien

cy

Eg (eV)

SQE

g - E

Cross=0.1 eV

Eg - E

Cross=0.3 eV

Eg - E

Cross=0.5 eV

i nn iP P

Interface recomb

qV

Efficiency Limits of Silicon Solar Cells

“Why Don’t We Have a 30% Efficient Si Solar Cell?” J.L. Gray and R.J. Schwartz, PVSC 1985

At a time of 19-20% cell…

4

1977

“Why We Will Have a 30% Efficient Si Solar Cell” R.M. Swanson, PVSC 1989

Opportunities for optimization of cell performance

Opportunities for Design

Back Contact Solar Cell (R.J. Schwartz, IEDM, 1975)

5

R.M. Swanson, “Point-contact solar cells: modeling and experiment” Solar Cells, 1985.

Exploring New Approaches

G. Lush & M. Lundstrom, “Thin film approaches for high-efficiency III-V cells” Solar Cells, 30 337 (1991).

G. B. Lush, et. al., “Microsecond lifetimes and low interface recombination velocities in moderately doped n-GaAsthin films,” APL, 61, 2441, 1992.

G.B. Lush, et al.,” Thin-film GaAs Solar Cells by Epitaxial Lift-off,” PVSC, 1993.

6

Cells by Epitaxial Lift-off,” PVSC, 1993.

Brendan M. Kayes, et al.,“27.6% Conversion Efficiency, A New Record for Single-junction Solar Cells Under 1-Sun Illumination,” PVSC 2011.

28.5% at 1-Sun

Too Complex to Model?

8

10

/cm

2 )

Nat. Mat, 2009

Organic Solar Cell CZTS

7

101

102

2

4

6

8

Anneal Time (min)

J SC

(mA

/cm

ta(opt)

(b)CIGS

Ramanathan , 2003

Guo , 2003

Grain boundaries, percolating cluster, anneal dependence, Na dependence …… problems beyond modeling?

Elements of end-to-end modeling

1) Process/Materials

2) Device5) Systems6) Software (ADEPT,

3) Characterization4) Reliability

End-to-end modeling of process/device/reliability/systems

Approaches applicable to multiple materials

Embedded in open source software platform

6) Software

Platform(ADEPT,

AMPS, PC1D)

8

(SCAPS)

Ex 1. End-to-End modeling of OPV

Organic PVTypical Cell

Nat. Mat, 2009

9

Morphological complexity is essential for device operation

Process Model: Demixing & Self-Organization

Polymer-A(Donor)

Solvent Polymer-B(Acceptor)

Free Energy, fmix

-0.2

0

0.2

0.4

ØA ØD100100100100100100100100

10

Phase Separation occurs through Spinodal Decomposition

10-15 min @125-150C Cahn-Hilliard Equation

2 40 2

ϕ κ ϕϕ

∂ ∂= ∇ − ∇ ∂ ∂

fM

t

Composition, Ø

Free Energy,

0 0.5 1-0.4

-0.2

X (nm)

Y (

nm

)

0 50 100

50

0

X (nm)

Y (

nm

)

0 50 100

50

0

X (nm)

Y (

nm

)

0 50 100

50

0

X (nm)

Y (

nm

)

0 50 100

50

0

X (nm)

Y (

nm

)

0 50 100

50

0

X (nm)

Y (

nm

)

0 50 100

50

0

X (nm)

Y (

nm

)

0 50 100

50

0

X (nm)

Y (

nm

)

0 50 100

50

0

Device Simulation & Experiments

00 Experiment [9]

4 min

Simulation (a) (b)

Experiment Simulation

11

0 0.2 0.4 0.6-10

-5

Voltage(V)0 0.2 0.4 0.6

-10

-5

Voltage(V)

J (m

A/c

m2 )

4 min

40 min

90 min

120 min

4 min

40 min

90 min

120 min

The limits of Jsc, Voc, and FF can be easily understood

Process Optimization: Annealing

Experiment Simulation

Optimum Anneal DurationWC ~Lex

WC

Late Anneal Phase(Coarse Morphology)

Early Anneal Phase(Sub- percolating Morphology)

WC >>Lex

101

102

1

1.5

2

2.5

3

Anneal Time (min)

Experiment

Anneal Time (min)

Efficiency (%)

101

102

1

1.5

2

2.5

3

ta(opt)

Simulation

12

Optimum anneal time predictable

JSC: Morphology & ReliabilityP

roce

ssin

g

Anneal duration, ta

Ta

=120

0 C

C

Stress duration, ts

102

103

3

3.5

4

4.5

5

J SC(m

A/cm

2)

ta (opt)

Low T

High T

Ts

=120

0 CStr

ess

Ts

=100

0 C

WC

102

103

Anneal time (min)

100

101

102

0.2

0.4

0.6

0.8

1 Ta= 500

J SC(norm

)

Stress time (h)

Ta= 800

SCJ ∝∝∝∝ n

eff opD t−−−−

∝∝∝∝ ex

C

L

W

Analytical Model

exp( / )eff A opD E kT∝ −∝ −∝ −∝ −

~ tn

thj

Anneal time

(b)Ex 1: End-to-End modeling of OPV

1) Process Model2) Device Sim.

0.8

1

(no

rm)

Ts= 800, 700, 600, 500 C

Electron

2) Device Sim.

3) Characterization4) Optimization

0 0.2 0.4 0.6-10

-5

0

Voltage(V)

J SC

(mA

/cm

2 ) 4 min

10 min20 min100 min

20

40

60

101

102

2

4

6

8

10

Anneal Time (min)

J SC(m

A/cm

2)

ta(opt)

5) Reliability

100

101

102

0.2

0.4

0.6

0.8

StressTime (h)

J SC

(no

rm)

exp. data [5]

14

Ex 2: ‘Technology-Agnostic’ approach to PV devices

P

D

R

S

C

15

Ex 2: ‘End-to-end Model of Shunt Conduction

AZO

a-Si:Hi

n

Process Model

p

P

D

R

S

C

16

(i)

(ii)

Device Model

CharacterizationReliability Model

Gap between Cell vs. Module Efficiency

0

5

10Power Distribution (mW)

0.75

0.99

CD

F (

norm

al)

1Ω/sq.4Ω/sq.8Ω/sq.

10X10 cm2

P

D

R

S

C

17

-15

-10

-5

0

Shunt distribution and series Rs explain the efficiency gap

6 7 8 9 10

0.01

0.25

0.75

Submodule Efficiency (%)

CD

F (

norm

al)

Cell

10X10 cm2

ERI-NPT Summary of research results 2010-11

1) Process

Model10

20

30

40

50

60

0

20

40

60

80

100

h(i,t

)

a-Si poly-crystal Image analysis

2) Devices:

Pol

ymer

thic

knes

s (n

m)

sc(max)

200

250

Database5) HIT Cell

a-Si p+-emitter

a-Si i-layer

c-Si n type

Pol

ymer

thic

knes

s (n

m)

ITO thickness (nm)100 200 300 400

50

100

150

200

CIGS with GB Optics

4) Compact model

Shunt statistics Module efficiency

Compact model

Si n type-B

ase

a-Si i-layer

a-Si n+-BSF c-si HIT-cell

3) Characterization:

18

Outline

Historical role of Modeling and Simulation in PV industry

Opportunities to contribute to end-to-end modelingExample 1: Organic Solar CellsExample 2: Shunt Conduction Example 2: Shunt Conduction

How to connect industry and academia by nanohub--- Simulation tools --- Tutorials and educational material

Conclusions

Network for Computational Nanotechnology

Mission:

Research that helps move nanoscience to nanotechnology to nanomanufacturing.

Infrastructure to:

• connect experts in theory, modeling, and

Key strategy:

Cyber-infrastructure

• connect experts in theory, modeling, and simulation and engage experimentalists

• disseminate software, insights and understanding, and research methods

• promote cross-disciplinary and simulation-based research and education

Established by NSF in 2002. 10-year, ~$30M investment

20

nanoHUB.org

a major, international resource for nanotechnology

>180,000 / year

enabled by the HUBzero platform for simulation, learning, and collaboration

1994

• open source platform

30% US / 24% Eur. / 35% Asia / 10% other

21

PVhub

https://nanohub.org/groups/pv

Welcome to PVhub!PVhub is a resource for the photovoltaics community. Our mission is to provide access to live simulations and resources for education and research. PVhub is an initiative of the Network for Photovoltaic Technology and the Network for Computational Nanotechnology. Technology and the Network for Computational Nanotechnology.

“I’d put my money on the sun and solar energy. What a source of power! I hope we don’t have to wait until oil and coal run out before we tackle that.” – Thomas Edison, 1931

22

Web enabled Tools

ADEPT PV Analyzer

OPV tool

PV Panel Sim

OPV tool

23

Education

24

Seminars

25

The Opportunity

Lessons from electronics:-importance of end-to-end modeling-need for shared platforms-M and S can have great impact

Strategy:

26

Strategy:-seek “technology agnostic solutions”-develop tools to solve problems-share tools with collaborators and sponsors-connect industry needs to university research-demonstrate the value of shared, pre-competitive research

Questions/comments: alam@purdue.edu