Abstract

Efficient quality control is inevitable in the manufacturing of light-emitting diodes (LEDs). Because defective LED chips may be traced back to different causes, a time and cost-intensive electrical and optical contact measurement is employed. Fast photoluminescence measurements, on the other hand, are commonly used to detect wafer separation damages but also hold the potential to enable an efficient detection of all kinds of defective LED chips. On a photoluminescence image, every pixel corresponds to an LED chip’s brightness after photoexcitation, revealing performance information. But due to unevenly distributed brightness values and varying defect patterns, photoluminescence images are not yet employed for a comprehensive defect detection. In this work, we show that fully convolutional networks can be used for chip-wise defect detection, trained on a small data-set of photoluminescence images. Pixel-wise labels allow us to classify each and every chip as defective or not. Being measurement-based, labels are easy to procure and our experiments show that existing discrepancies between training images and labels do not hinder network training. Using weighted loss calculation, we were able to equalize our highly unbalanced class categories. Due to the consistent use of skip connections and residual shortcuts, our network is able to predict a variety of structures, from extensive defect clusters up to single defective LED chips.

中文翻译：

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利用光致发光图像的全卷积网络芯片方向缺陷检测

摘要

在制造发光二极管（LED）时，有效的质量控制是不可避免的。由于有缺陷的LED芯片可追溯到不同的原因，因此需要花费大量时间和成本的电气和光学接触测量。另一方面，快速的光致发光测量通常用于检测晶圆分离损伤，但也具有实现高效检测各种有缺陷的LED芯片的潜力。在光致发光图像上，每个像素对应光激发后LED芯片的亮度，从而显示性能信息。但是由于亮度值分布不均和缺陷图案变化，光致发光图像尚未用于全面的缺陷检测。在这项工作中，我们证明了完全卷积网络可用于芯片级缺陷检测，在少量的光致发光图像数据集上进行训练。逐像素标签允许我们将每个芯片分类为有缺陷或无缺陷。标签是基于测量的，易于采购，我们的实验表明，训练图像和标签之间的现有差异不会阻碍网络训练。使用加权损失计算，我们可以均衡高度不平衡的类别。由于始终使用跳过连接和残留的快捷方式，因此我们的网络能够预测从广泛的缺陷簇到单个缺陷LED芯片的各种结构。标签易于购买，我们的实验表明，训练图像和标签之间的现有差异不会阻碍网络训练。使用加权损失计算，我们可以均衡高度不平衡的类别。由于始终使用跳过连接和剩余的快捷方式，因此我们的网络能够预测从广泛的缺陷簇到单个缺陷LED芯片的各种结构。标签易于购买，我们的实验表明，训练图像和标签之间的现有差异不会阻碍网络训练。使用加权损失计算，我们可以均衡高度不平衡的类别。由于始终使用跳过连接和残留的快捷方式，因此我们的网络能够预测从广泛的缺陷簇到单个缺陷LED芯片的各种结构。