Paper Info
Title
Contextual Anomaly Detection in Solder Paste Inspection with Multi-Task Learning
Abstract
AbstractIn this article, we study solder paste inspection (SPI), an important stage that is used in the semiconductor manufacturing industry, where abnormal boards should be detected. A highly accurate SPI can substantially reduce human expert involvement, as well as reduce the waste in disposing of the boards in good condition. A key difference today is that because of increasing demand in board customization, the number of board types increases substantially and quantity of the boards produced in each type decreases. Thus, the previous approaches where a fine-tuned model is developed for each board type are no longer viable.Intrinsically, our problem is an anomaly detection problem. A major specialty in today’s SPI is that the target tasks for prediction cannot be fully pre-determined due to context changes during the solder paste printing stage. Our experiences show that a conventional approach to first define a set of tasks and train these tasks offline will lead to low accuracy. Here, we propose a novel multi-task approach, where the performance of all target tasks is ensured simultaneously. We note that the SPI process is streamlined and automatic, allowing the SPI time for only a few seconds. We propose a fast clustering algorithm that reuses existing models to avoid retraining and fine tune in the inference phase. We evaluate our approach using 3-month data collected from production lines. We show that we can reduce 81.28% of false alarms. This can translate to annual savings of $11.3 million.
Year
DOI
Venue
2020
10.1145/3383261
ACM Transactions on Intelligent Systems and Technology
Keywords
DocType
Volume
Contextual anomaly detection, multi-task learning
Journal
11
Issue
ISSN
Citations 
6
2157-6904
0
PageRank 
References 
Authors
0.34
16
7
Name
Order
Citations
PageRank
Zimu Zheng1226.00
Jie Pu200.34
Linghui Liu300.34
Dan Wang468658.70
Xiangming Mei500.34
Sen Zhang600.34
Quanyu Dai7285.28