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Vision-driven Autocharacterization of Perovskite Semiconductors

Vision-driven Autocharacterization of Perovskite Semiconductors


Authors: Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen, Hamide Kavak, Basita Das, and Tonio Buonassisi

Abstract

In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly accelerating the throughput of the workflow for scientists. We present a framework that uses vision to address specific pain points in

the characterization of perovskite semiconductors, a group of materials with the potential to form new types of solar cells. With this approach, we automate the measurement

and computation of chemical and optoelectronic properties of perovskites. Our framework proposes the following four key contributions: (i) a computer vision tool for scalable segmentation to arbitrarily many material samples, (ii) a tool to extract the chemical composition of all material samples, (iii) an algorithm capable of automatically

computing band gap across arbitrarily many unique samples using vision-segmented hyperspectral reflectance data, and (iv) automating the stability measurement

of multi-hour perovskite degradation experiments with vision for spatially non-uniform samples. We demonstrate the key contributions of the proposed framework on eighty

samples of unique composition from the formamidiniummethylammonium lead tri-iodide perovskite system and validate the accuracy of each method using human evaluation

and X-ray diffraction.



Fig. The workflow for autocharacterization of perovskite semiconductors using computer vision. From left to right: A fluid handling system mixes and deposits each unique semiconductor material onto a plate. The vision system captures images in RGB and hyperspectral channels (wavelengths λ ϵ [380nm; 1020nm]), then segments the pixels of each material deposit (X; Y; λ) from the background. The (i) compositional information, (ii) direct band gap, and (iii) stability of each unique sample are automatically characterized using the optical data segmented by the vision system.


Selected Figures




Keywords: High-throughput laboratory automation; scalable computer vision segmentation; perovskite composition engineering; optical band gap; stability measurement; pinch valve


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