Improved evaluation of deep-level transient spectroscopy on perovskite solar cells reveals ionic defect distribution

TL;DR

This study improves the analysis of deep-level transient spectroscopy on perovskite solar cells, revealing that ionic defects exhibit a distribution of emission rates, which explains variability in defect parameters and enhances understanding of ion migration effects.

Contribution

The paper introduces an extended regularization algorithm for DLTS data inversion, uncovering ionic defect distributions in perovskite solar cells and validating the method across different DLTS modes.

Findings

Ionic defects form a distribution of emission rates.

Different DLTS modes yield consistent defect distributions.

The new algorithm is validated against conventional methods.

Abstract

One of the key challenges for future development of efficient and stable metal halide perovskite solar cells is related to the migration of ions in these materials. Mobile ions have been linked to the observation of hysteresis in the current--voltage characteristics, shown to reduce device stability against degradation and act as recombination centers within the band gap of the active layer. In the literature one finds a broad spread of reported ionic defect parameters (e.g. activation energies) for seemingly similar perovskite materials, rendering the identification of the nature of these species difficult. In this work, we performed temperature dependent deep-level transient spectroscopy (DLTS) measurements on methylammonium lead iodide perovskite solar cells and developed a extended regularization algorithm for inverting the Laplace transform. Our results indicate that mobile ions form a distribution of emission rates (i.e. a distribution of diffusion constants) for each observed ionic species, which may be responsible for the differences in the previously reported defect parameters. Importantly, different DLTS modes such as optical and current DLTS yield the same defect distributions. Finally the comparison of our results with conventional boxcar DLTS and impedance spectroscopy (IS) verifies our evaluation algorithm.