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Machine Vision for Solar Cell Inspection

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2016 ASEE Annual Conference & Exposition


New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

June 29, 2016





Conference Session

Latest Trends and Implementations in Manufacturing Education

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Paper Authors


Michael G. Mauk Drexel University

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Michael Mauk is Assistant Professor in Drexel University's Engineering Technology program.

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Richard Chiou Drexel University

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Dr. Richard Chiou is Associate Professor within the Engineering Technology Department at Drexel University, Philadelphia, USA. He received his Ph.D. degree in the G.W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. His educational background is in manufacturing with an emphasis on mechatronics. In addition to his many years of industrial experience, he has taught many different engineering and technology courses at undergraduate and graduate levels. His tremendous research experience in manufacturing includes environmentally conscious manufacturing, Internet based robotics, and Web based quality. In the past years, he has been involved in sustainable manufacturing for maximizing energy and material recovery while minimizing environmental impact.

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Chetana R. Bayas

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Solar cells in various stages of production, e.g., silicon feedstock; polycrystalline castings; single and multicrystalline wafers and sheets; and solar cells with texture etching, screen-printed metal contacts, and anti-reflection coatings offer instructive examples for using machine vision for quality assurance, materials and device characterization, and solar cell diagnostics. The solar cell proves an effective ‘vehicle’ for demonstrating and exploring various aspects of materials science (grain size and texture and other features of microstructure, optical manifestations of defects, metallurgy of electrical contacts), thin-film technology (screen printing, coatings, texturization, optics, surface metrology, rational cleaning methods, Further, the solar cell fabrication processes are typically automated with conveyer belts and robotics, offering many industrially-relevant opportunities for applying machine vision techniques for inspection, process control, and product sorting. In this paper, we describe various aspects of image capture, processing, and analysis using low-cost CCD cameras and thermal infrared cameras as instructive case studies in materials science, thin film technology, and machine vision for quality assurance.

Mauk, M. G., & Chiou, R., & Bayas, C. R. (2016, June), Machine Vision for Solar Cell Inspection Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.27325

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