Publication date: 22nd July 2026
In many emerging solar cell technologies, it is a significant challenge to extract the electronic properties of materials and interfaces inside a working device from experimental data. Traditionally, the properties of materials or interfaces within a device are directly measured using advanced characterization methods. However, most methods applied to individual layers measure the properties of the material in isolation, not in the context of the device. Methods that measure the properties of the entire device cannot measure the properties of the individual layers and hence cannot identify which of the layers in a device stack might be the root cause of underperformance in a device under study. To circumvent the challenges in insight generation we implemented a method for parameter estimation using Bayesian Inference with accelerated sampling (BIAS) and demonstrated its capabilities by extracting physical parameters from illumination dependent current-voltage data of a perovskite solar cell (PSC). This insights can then be used to make informed decisions about strategies for process and material innovations.
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