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We present a novel approach called MINiature Interactive Offset Networks (or MINIONs). We use wafer map classification as an application example. A Minion is trained with a specially-designed one-shot learning scheme. A collection of Minions can be used to patch a master model. Experiment results are provided to explain the potential areas Minions can help and their unique benefits.
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Learning A Wafer Feature With One Training Sample
In this work, we consider learning a wafer plot recognizer where only one training sample is available. We introduce an approach called Manifestation Learning to enable the learning. The underlying technology utilizes the Variational AutoEncoder (VAE) approach to construct a so-called Manifestation Space. The training sample is projected into this space and the recognition is achieved through a pre-trained model in the space. Using wafer probe test data from an automotive product line, this paper explains the learning approach, its feasibility and limitation.
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- Award ID(s):
- 2006739
- PAR ID:
- 10295363
- Date Published:
- Journal Name:
- 2020 IEEE International Test Conference (ITC)
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ITC44778.2020.9325254
