Year: 2025
Type: conference-paper
Abstract: Stacking faults in epitaxial silicon wafers are structural defects that can reduce the recombination lifetime of the final solar cells significantly. They are known to originate mostly at the interface between substrate and deposited layer, at contamination particles and atomic steps. This work presents a non-destructive and automated characterization method on full-size wafers to locate stacking faults and determine their layer of origin to identify process-based root causes. A deep learning model and a quantification via geometric defect properties is realized on dark field microscope images, with the potential to be transferred to inline images measured in dark field mode with high-resolution cameras. We achieve detection rates up to 92% for regular wafer surfaces. The depth analysis combines geometric properties of the stacking faults and measured wafer thickness and is applied on full-scale epitaxial wafers. Most stacking faults are confirmed to originate at the interface layer and their number is higher by 1-2 orders of magnitude when deposition occurs on a reorganized porous layer. However, our results also indicate that a non-negligible part of stacking faults has its origin within the epitaxial layer.
Source: SiliconPV Conference Proceedings
Authors: Theresa Trötschler, Saed Al-Hajjawi, Siddharth Raghavendran, Jonas Haunschild, Matthias Demant, Stefan Rein
Institution: Fraunhofer Institute for Solar Energy Systems
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Award: G5515105064
OpenAlex ID: W4407101770