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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.

Nonuniform residual stresses may develop during anodic bonding due to variations in wafer curvature prior to bonding or due to nanotopography interactions between the bonding surfaces. In this work, we discuss the significance of nonuniform residual stress in anodically bonded wafer pairs and present a method for measuring local stress variations even in cases when the bonded wafers are apparently defect free based on conventional inspection tools. These residual stresses can vary significantly between two different wafers processed identically, depending on the local interactions, but they are present in all standard bonding methodologies. In some instances, these local residual stresses can significantly alter the resulting wafer curvature in apparently defect free substrates. Currently, anodically bonded devices or substrate wafers are considered "defect free" if no large debonds are found after bonding; however the residual stress state of the wafers cannot be determined using the same conventional techniques.