THESIS
2024
1 online resource (xi, 54 pages) : illustrations (some color)
Abstract
In recent years, we have witnessed many excellent machine learning (ML) solutions targeting
circuit layouts with the emerging of AI. Researchers have developed more and more
ML models for hotspot detection in chip layout. These ML models have demonstrated
super potential in classification and can provide very fast predictions on various design objectives.
However, almost all existing ML solutions for hotspot detection have neglected the
basic interpretability requirement from potential users that could cause taping-out failure.
As a result, it is very difficult for users to figure out any potential accuracy degradation or
abnormal behaviors of given ML models.
In this work, a new technique named APPLE1 is proposed to explain each ML prediction
at the resolution level of circuit elements...[
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In recent years, we have witnessed many excellent machine learning (ML) solutions targeting
circuit layouts with the emerging of AI. Researchers have developed more and more
ML models for hotspot detection in chip layout. These ML models have demonstrated
super potential in classification and can provide very fast predictions on various design objectives.
However, almost all existing ML solutions for hotspot detection have neglected the
basic interpretability requirement from potential users that could cause taping-out failure.
As a result, it is very difficult for users to figure out any potential accuracy degradation or
abnormal behaviors of given ML models.
In this work, a new technique named APPLE1 is proposed to explain each ML prediction
at the resolution level of circuit elements and we also employ guided backpropogation
method to interpret convolutional neural network (CNN) model’s focus region by leveraging
gradient-based analysis. According to current knowledge, this is the first effort to
explain ML predictions on circuit layouts. This framework provides a significantly more
reasonable, useful, and efficient explanation for lithography hotspot prediction, compared
with the highest-cited prior solution for natural images. And this holds significant practical
value such as preventing backdoor attack.
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