In modern manufacturing, most of the product lines are conforming. Few
products are nonconforming but with different defect types. The identification
of defect types can help further root cause diagnosis of production lines. With
the sensing development, continuous signals of process variables can be
collected in high resolution, which can be regarded as multichannel functional
data. They have abundant information to characterize the process and help
identify the defect types. Motivated by a real example from the pipe tightening
process, we target at detect classification when each sample is a multichannel
functional data. However, the available samples for each defect type are
limited and imbalanced. Moreover, the functions are partially observed since
the pre-tightening process before the pipe tightening process is unobserved. To
classify the defect samples based on imbalanced, multichannel, and partially
observed functional data is very important but challenging. Thus, we propose an
innovative framework known as "Multichannel Partially Observed Functional
Modeling for Defect Classification with an Imbalanced Dataset" (MPOFI). The
framework leverages the power of deep metric learning in conjunction with a
neural network specially crafted for processing functional data. This paper
introduces a neural network explicitly tailored for handling multichannel and
partially observed functional data, complemented by developing a corresponding
loss function for training on imbalanced datasets. The results from a
real-world case study demonstrate the superior accuracy of our framework when
compared to existing benchmarks.

利用深度度量学习和专门设计用于处理多通道和部分观测功能数据的神经网络，我们提出了一种名为 MPOFI 的创新框架，用于在一个不平衡数据集中对缺陷样本进行分类。通过开发相应的损失函数，我们展示了该框架相对于现有基准的卓越准确性。

MPOFI：基于深度度量学习的多通道部分观测功能建模用于不平衡数据集的缺陷分类

MPOFI: Multichannel Partially Observed Functional Modeling for Defect  Classification with Imbalanced Dataset via Deep Metric Learning

Detecting and analyzing various defect types in semiconductor materials is an
important prerequisite for understanding the underlying mechanisms as well as
tailoring the production processes. Analysis of microscopy images that reveal
defects typically requires image analysis tasks such as segmentation and object
detection. With the permanently increasing amount of data that is produced by
experiments, handling these tasks manually becomes more and more impossible. In
this work, we combine various image analysis and data mining techniques for
creating a robust and accurate, automated image analysis pipeline. This allows
for extracting the type and position of all defects in a microscopy image of a
KOH-etched 4H-SiC wafer that was stitched together from approximately 40,000
individual images.

通过融合各种图像分析和数据挖掘技术，我们在这项工作中创建了一个强大且准确的自动化图像分析流水线，用于提取显微图像中 KOH 刻蚀的 4H-SiC 晶片的所有缺陷的类型和位置。