Some crystalline defects in photovoltaic silicon have deleterious effects on the energy conversion efficiency of the material. Distinguishing the harmful defects from the benign defects is a critical problem in the mechanics of materials for solar energy conversion. Interestingly, the visible light absorbed by silicon in the same part of the solar spectrum that is used to generate photocurrent, can also excite photoluminescence, which may be used to generate images of the microstructure. Slightly longer wavelengths in the near infrared (IR) may be used to measure strain in the material via photoelastic (PE) imaging. These two imaging modalities have recently been combined in a single instrument, and we show here the additional capability to identify and categorize defects directly by capturing the narrow band of photoluminescence emitted by regions of high dislocation density. We use this method to show that dislocations arranged in low angle grain boundaries emit polarized light, while dislocation structures in neighboring high angle grain boundaries do not emit polarized light. This capability may form the basis for nextgeneration, full-field optomechanics-based characterization of materials for solar energy conversion.
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 from 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 direction of wire motion than when loaded along the direction of wire motion. 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.
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