1. 1 The Role of Simulation in Photovoltaics: From Solar Cells To Arrays Ricardo Borges, Kurt Mueller, and Nelson Braga Synopsys, Inc.

2. 2 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

3. 3 Addressing Issues at All Stages ModuleSystemCell 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 Synopsys Saber tools Synopsys TCAD tools

4. 4 What is TCAD? Device Simulation Current in Drift-Diffusion Model Potential distribution in flash memory Snapback of a UMOS EM Wave Inductance Simulation AlGaAs VCSEL Full Chip H-Bridge Process Simulation LDMOS: doping, mesh 1D doping profile simulation PDE for Pair Diffusion Mechanical stress in intermetal dielectric Photogeneration in CIS PVD (Physical Vapor Deposition)

5. 5 Continuous innovation makes cells more complex –More process and geometrical variables –3D effects, complex light path, etc … Its impractical to design new cells without simulation –Too many experiments are needed to investigate design space –Risks missing optimum design and market window Why Simulate Solar Cells? Early generation cell (Eff ~ 15-16%) New generation cell (Eff ~ 20%) Source: SERIS

6. 6 Solar Cell Simulation Flow SimulationOutputInput Device Geometry (doping profiles) Process Simulation Process Flow Recipe External reflection Optical generation Optical Simulation Optical Data: n & k Device Geometry Dark & Light I-V IQE, EQE Electrical Simulation Electrical Data: SRH, Auger, BGN, Mobility Device Geometry (lifetime, doping profiles)

7. 7 Select parameters to be investigated Parameterize the TCAD model Run simulations Visualize the influence of each parameter Example: 2D Cell Optimization d sub w back w tot SfSf SbSb N lfsf N bulk d lbsf N lbsf d bsf d lfsf t bulk W front

8. 8 w front w back w tot d sub N bulk d bsf N lbsf d lbsf N lfsf d lfsf t bulk S f S b j sc V oc FF eff Example: Unit Cell Optimization Results 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

9. 9 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 Source: F. Granek et al, Progress in Photovoltaics: Research and Applications, 17, Oct 2008, pp 47-56 Antireflection Coating (ARC) n+ BSF n+ FSF Base (n-type) p+ emitter Passivation Surface Texturing Metal Contacts Pitch

10. 10 Application: Multi-Junction Solar Cells Source: Philipps, S.S. et.al.. NUMERICAL SIMULATION AND MODELING OF III-V MULTI-JUNCTION SOLAR CELLS Proceedings of 23rd EUPVSEC, 2008 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

11. 11 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

12. 12 What is Saber? Multi-domain circuit simulation… Control Algorithms Multiple Domains Behavioral Models enabling full system Virtual Prototyping Nominal Design Parameter Variation Production Tolerances Statistical Analyses Fault Analyses Power Electronics Worst- Case Optimizing System Performance and Reliability

13. 13 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

14. 14 Module Optimization From system cell level model, sweeps can be done to determine the effect of different cell widths on module performance Allows for optimization of Maximum Power Point at a module level as a function of luminance and cell width Module Optimization: Variation of equivalent R Series & R Shunt I Module (A) V V

15. 15 Module Validation Accurate, physics-based models take TCAD results to system simulation for validating real-world measurements

16. 16 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 Photovoltaic Module Performance Verification at Different Cell Temperatures Measurement of MPPT at Different Temperatures

17. 17 Power Electronics Control System & Algorithms Environment System Integration & Optimization Simulation provides integrated test, validation and optimization environment for all aspects of the system:

18. 18 Battery Charging System Simulation System highlights: Maximum Power Point Tracking through impedance matching using controlled DC/DC converter Dynamic thermal capable array model

19. 19 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

20. 20 Predictable Success