10.02.2016 | Research & Development

Jonas & Redmann conducts research with the Fraunhofer Institute for Solar Energy Systems ISE and the Fraunhofer Center for Silicon Photovoltaics CSP as part of the leading-edge cluster “Solarvalley Mitteldeutschland”

The primary technical objective of the joint project was the reduction of “specific” solar cells costs (quotient: “cost / peak power” [€/WP]). To achieve this, higher-efficiency solar cells were developed from thinner wafers, since they have great potential to reduce production costs thanks to the material savings, while also increasing cell efficiency. Thinner wafers and cells and the subsequently modified and new technological processing also inevitably bring with them changes in the mechanical properties. While the ultimate objective of the cluster is to change wafers and cells, the required handling steps must nevertheless remain automated. In dealing with this issue, Jonas & Redmann can make use of 26 years of engineering and production experience in high-tech industries. Since 1999, the company has developed technologies which first made the industrial production of crystalline silicon solar cells possible. In 2000, Jonas & Redmann set a first milestone with the automation of a complete production line for c-Si solar cells. Since then, the company has developed numerous important – including some patented – handling and transportation solutions for the PV industry. This includes the Jonas & Redmann back-to-back handling, the Jonas & Redmann Bernoulli gripper, the Jonas & Redmann automation carrier, and the Jonas & Redmann wafer magazine.


Microcracks in Silicon Wafers I: Inline Detection and Implications of Crack Morphology on Wafer Strength

Microcracks in silicon wafers reduce the strength of the wafers and can lead to critical failure within the solar-cell production. Both detection of the microcracks and their impact on fracture strength of the wafers are addressed within this study. To improve the accuracy of the crack detection in photoluminescence (PL) and infrared transmission (IR) images of as-cut wafers, we introduce a pattern recognition approach based on local descriptors and support-vector classification. The learning model requires a set of labeled data generated by an artificial insertion of cracks. Within this evaluation, the algorithm detects 81% of the cracks for PL-images and 98% for IR-images at precision rates above 98% in each case, which outperforms the quality of pure IR-intensity-based crack-detection systems with a hit-rate of 65% at a precision of 59%. The proposed algorithm may be combined with the images of the grain structure to avoid the confusion of cracks and grain boundaries. Moreover, the comprehensive set of wafers allows the impact of crack morphology on wafer strength to be investigated. Despite complex crack morphologies, the theoretically expected dependence between crack length and fracture strength is confirmed. Therefore, sorting criteria are derived to rate the cracks with respect to the expected fracture strength of the wafer based on the measured crack length only.

Published in:

Photovoltaics, IEEE Journal of  (Volume:6 ,  Issue: 1)

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Kontakt Jonas & Redmann:

Sebastian Bartsch
Forschung & Entwicklung

Elke Beune
Corporate Communications
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