Nondestructive Photoelastic and Machine Learning Characterization of Surface Cracks and Prediction of Weibull Parameters for Photovoltaic Silicon Wafers

Logan P. Rowe, Alexander J. Kaczkowski, Tung Wei Lin, Gavin P. Horn, Harley T. Johnson

Research output: Contribution to journalArticlepeer-review

Abstract

A nondestructive photoelastic method is presented for characterizing surface microcracks in monocrystalline silicon wafers, calculating the strength of the wafers, and predicting Weibull parameters under various loading conditions. Defects are first classified through thickness infrared photoelastic images using a support vector machine-learning algorithm. Characteristic wafer strength is shown to vary with the angle of applied uniaxial tensile load, showing greater strength when loaded perpendicular to the wire speed direction than when loaded along the wire speed direction. Observed variations in characteristic strength and Weibull shape modulus with applied tensile loading direction stem from the distribution of crack orientations and the bulk stress field acting on the microcracks. Using this method, it is possible to improve manufacturing processes for silicon wafers by rapidly, accurately, and nondestructively characterizing large batches in an automated way.

Original languageEnglish (US)
Article number031001
JournalJournal of Engineering Materials and Technology, Transactions of the ASME
Volume144
Issue number3
DOIs
StatePublished - Jul 1 2022

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

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