基于光致发光图像的全卷积网络芯片级缺陷检测
Fully Convolutional Networks for Chip-wise Defect Detection Employing Photoluminescence Images
摘要：
在发光二极管（LED）的制造中，高效的质量控制是不可避免的。由于有缺陷的LED芯片可以追溯到不同的原因，因此采用时间和成本密集的电气和光学接触测量。另一方面，快速光致发光测量通常用于检测晶片分离损伤，但也有可能有效检测各种缺陷LED芯片。在光致发光图像上，每个像素对应于光激发后LED芯片的亮度，揭示了性能信息。但由于亮度值分布不均匀和缺陷图案变化，光致发光图像尚未用于全面的缺陷检测。在这项工作中，我们证明了全卷积网络可以用于芯片级缺陷检测，并在光致发光图像的小数据集上进行训练。像素标签允许我们将每个芯片分类为有缺陷或没有缺陷。基于测量，标签很容易获得，我们的实验表明，训练图像和标签之间存在的差异不会阻碍网络训练。使用加权损失计算，我们能够平衡我们高度不平衡的类别。由于一直使用跳过连接和剩余捷径，我们的网络能够预测各种结构，从广泛的缺陷集群到单个缺陷LED芯片。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
