Local Defects in Colloidal Quantum Dot Thin Films Measured via Spatially Resolved Multi‐Modal Optoelectronic Spectroscopy

• Published in: Advanced materials (Weinheim) 2020-03, Vol.32 (11), p.e1906602-n/a
• Main Authors: Lin, Yida, Gao, Tina, Pan, Xiaoyun, Kamenetska, Maria, Thon, Susanna M.
• Format: Article
• Language: English
• Subjects: Chemical composition; colloidal quantum dots; Contaminants; defect characterization; Defects; Field of view; Identification methods; macroscopic profiling; Mapping; Materials science; Morphology; Optoelectronics; Photoelectric effect; Photoelectric emission; Photoluminescence; Photovoltaic cells; Quantum dots; scanning microscopy; Solar cells; Spectrum analysis; Statistical methods; Thin films
• ISSN: 0935-9648; 1521-4095
• DOI: 10.1002/adma.201906602

The morphology, chemical composition, and electronic uniformity of thin‐film solution‐processed optoelectronics are believed to greatly affect device performance. Although scanning probe microscopies can address variations on the micrometer scale, the field of view is still limited to well under the typical device area, as well as the size of extrinsic defects introduced during fabrication. Herein, a micrometer‐resolution 2D characterization method with millimeter‐scale field of view is demonstrated, which simultaneously collects photoluminescence spectra, photocurrent transients, and photovoltage transients. This high‐resolution morphology mapping is used to quantify the distribution and strength of the local optoelectronic property variations in colloidal quantum dot solar cells due to film defects, physical damage, and contaminants across nearly the entire test device area, and the extent to which these variations account for overall performance losses. It is found that macroscopic defects have effects that are confined to their localized areas, rarely prove fatal for device performance, and are largely not responsible for device shunting. Moreover, quantitative analysis based on statistical partitioning methods of such data is used to show how defect identification can be automated while identifying variations in underlying properties such as mobilities and recombination strengths and the mechanisms by which they govern device behavior. A 2D multi‐modal scanning spectroscopy method that is able to simultaneously measure multiple device parameters of interest, including photoluminescence, photovoltage, and photocurrent, is demonstrated and used to examine the distribution, strength, and correlation of local variations of optoelectronic properties in colloidal quantum dot solar cells. Additionally, statistical partitioning methods are used to automatically identify macroscopic film defects.