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

IF 1.5 4区 材料科学 Q3 ENGINEERING, MECHANICAL Journal of Engineering Materials and Technology-transactions of The Asme Pub Date : 2021-10-08 DOI:10.1115/1.4052673
L. Rowe, Alexander J. Kaczkowski, T. Lin, G. Horn, H. Johnson
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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 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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光伏硅片表面裂纹的无损光弹性和机器学习表征及威布尔参数预测
提出了一种无损光弹性方法来表征单晶硅片表面微裂纹,计算硅片的强度,并预测不同加载条件下的威布尔参数。首先使用支持向量机学习算法从全厚度红外光弹性图像中对缺陷进行分类。晶片的特征强度随所施加的单轴拉伸载荷的角度而变化,当垂直于金属丝运动方向加载时比沿金属丝运动的方向加载时显示出更大的强度。观察到的特征强度和威布尔形状模量随拉伸载荷方向的变化源于裂纹方向的分布和作用在微裂纹上的体积应力场。使用这种方法,可以通过以自动化的方式快速、准确和无损地表征大批量硅片,从而改进硅片的制造工艺。
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来源期刊
CiteScore
3.00
自引率
0.00%
发文量
30
审稿时长
4.5 months
期刊介绍: Multiscale characterization, modeling, and experiments; High-temperature creep, fatigue, and fracture; Elastic-plastic behavior; Environmental effects on material response, constitutive relations, materials processing, and microstructure mechanical property relationships
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