ACS Applied Materials & Interfaces · 2019 · 125 citations · 46 references
EngineeringMaterials EngineeringMaterials ScienceElectronic MaterialsSemiconductor DevicesSemiconductor MaterialsDevice ModelingDevice SimulationPerovskite MaterialsPhotovoltaic DevicesPhotovoltaic MaterialsSolar CellsCharge Carrier DynamicsCharge InjectionCharge TrapsIonic DefectsOpen-circuit VoltageSolar Cell Device PhysicsTransient Device ModelingTraps
A variety of experiments on vacuum-deposited methylammonium lead iodide perovskite solar cells are presented, including JV curves with different scan rates, light intensity-dependent open-circuit voltage, impedance spectra, intensity-modulated photocurrent spectra, transient photocurrents, and transient voltage step responses. All these experimental data sets are successfully reproduced by a charge drift-diffusion simulation model incorporating mobile ions and charge traps using a single set of parameters. While previous modeling studies focused on a single experimental technique, we combine steady-state, transient, and frequency-domain simulations and measurements. Our study is an important step toward quantitative simulation of perovskite solar cells, leading to a deeper understanding of the physical effects in these materials. The analysis of the transient current upon voltage turn-on in the dark reveals that the charge injection properties of the interfaces are triggered by the accumulation of mobile ionic defects. We show that the current rise of voltage step experiments allow for conclusions about the recombination at the interface. Whether one or two mobile ionic species are used in the model has only a minor influence on the observed effects. A delayed current rise observed upon reversing the bias from +3 to -3 V in the dark cannot be reproduced yet by our drift-diffusion model. We speculate that a reversible chemical reaction of mobile ions with the contact material may be the cause of this effect, thus requiring a future model extension. A parameter variation is performed in order to understand the performance-limiting factors of the device under investigation.
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