THESIS
2023
1 online resource (xiv, 90 pages) : illustrations (some color)
Abstract
The semiconductor industry is critical in driving technological advancements in
various fields. Ensuring the production of high-quality semiconductor devices is
critical to maintaining reliability and performance. Traditional manual inspection
methods are time-consuming and expensive, which leads to an increasing
need to develop automated systems that can efficiently and accurately detect
abnormalities in semiconductor devices. However, there are many challenges in
developing machine learning inspection systems for real-world production, from
ensuring high-quality training data to designing appropriate algorithms to solve
specific problems in the data. When preparing datasets for data-driven solutions,
manual labeling is prone to human labeling errors, directly affecting detection
syste...[
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The semiconductor industry is critical in driving technological advancements in
various fields. Ensuring the production of high-quality semiconductor devices is
critical to maintaining reliability and performance. Traditional manual inspection
methods are time-consuming and expensive, which leads to an increasing
need to develop automated systems that can efficiently and accurately detect
abnormalities in semiconductor devices. However, there are many challenges in
developing machine learning inspection systems for real-world production, from
ensuring high-quality training data to designing appropriate algorithms to solve
specific problems in the data. When preparing datasets for data-driven solutions,
manual labeling is prone to human labeling errors, directly affecting detection
systems’ accuracy. Even if the inspection system is perfectly trained on a particular
batch of data, when the distribution of the test data changes, such as a new
fault type that has yet to be learned, the model will not work correctly. Implementing
machine learning involves iterative cycles, requiring continuous training
and evaluation.
This paper comprehensively studies the existing inspection system and analyzes
its difficulties. The existing inspection system has a rejection rate of 0.5%. Due to the enormous amount of semiconductors produced daily, more than one million
are discarded, but only less than 0.01% of them are actually defective. We
aim to reduce the false positive rate and collect those ”hard samples” rejected
by existing inspection systems. We then address this problem using a variety
of machine learning techniques, ranging from traditional supervised image classification,
self-supervised contrastive learning, and semi-supervised self-training
to unsupervised reconstruction-based learning, focusing on dealing with noisy labels
caused by human factors and data drift caused by marking codes of different
production batches.
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