Local Defects in Colloidal Quantum Dot Thin Films Measured via Spatially Resolved Multi‐Modal Optoelectronic Spectroscopy
Local Defects in Colloidal Quantum Dot Thin Films Measured via Spatially Resolved Multi‐Modal Optoelectronic Spectroscopy
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DOI：
10.1002/adma.201906602
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发表时间：
2020-02
影响因子：
29.4
通讯作者：
Yida Lin;Tina Gao;Xiaoyun Pan;M. Kamenetska;S. Thon
中科院分区：
文献类型：
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作者：
Yida Lin;Tina Gao;Xiaoyun Pan;M. Kamenetska;S. Thon
薄膜溶液处理光电子器件的形貌、化学成分和电子均匀性被认为对器件性能有很大影响。尽管扫描探针显微镜可以处理微米尺度上的变化，但其视野仍然局限于典型的器件区域，以及在制造过程中引入的外部缺陷的尺寸。本文提出了一种毫米尺度视场的微米分辨率二维表征方法，该方法可以同时收集光致发光光谱、光电流瞬态和光电压瞬态。这种高分辨率形态映射用于量化胶态量子点太阳能电池中由于薄膜缺陷、物理损伤和几乎整个测试设备区域的污染物而导致的局部光电特性变化的分布和强度，以及这些变化对整体性能损失的影响程度。研究发现，宏观缺陷的影响局限于局部区域，很少对器件性能造成致命影响，并且在很大程度上不会导致器件分流。此外，基于这些数据的统计划分方法的定量分析被用于显示缺陷识别如何能够自动化，同时识别诸如移动性和重组强度以及它们控制设备行为的机制等潜在属性的变化。
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.