Tensor Decomposition for Concept Recognition
- Author(s): Wahba, Ahmed
- Advisor(s): Wang, Li-C.
- et al.
The yield optimization data analytic process is an iterative search process. Each iteration
comprises of two main steps: data preparation, and result evaluation. In this thesis, we
focus on the result evaluation step, where the analyst evaluates the result from running
a certain analytic task looking for some specic concept. For example this concept can
be in the form a pattern formed by the failing dies on a wafer.
A wafer pattern may hint a particular issue in the production by itself or guide the
analysis into a certain direction. In this thesis, we show how Generative Adversarial
Networks (GANs) can be used to build concept recognizers, we present the architecture
chosen for the convolutional neural networks, we show how the network is trained, and
we show how the discriminator network can be used for concept recognition.
However, the main focus of this work is on rules of Tensor decomposition in the
automated concept recognition
ow. We introduce a novel concept recognition approach
based on Tucker decomposition. Tensor-based concept recognizers are combined with
GANs-based recognizers to prevent escapes, also known as adversarial examples.
In this work, we are concerned with two main aspects: (1) automation of the concept
recognition process, and (2) ensuring robustness. Two tensor methods are introduced to
address both aspects.
The rst tensor method is based on clustering and is used to automatically extract
concepts that might be of interest to the analyst as well as choose the set of wafers to be
used in training for each concept.
Our second tensor method is to ensure robustness of the GANs-based recognizers
by introducing the containment check to continuously check for GANs performance and
report to the expert if a problem occurs.
We also address other challenges, such as automating the training process for GANs.
We also introduce a collaboration view where the machine learning expert is assumed
to be a separate entity. Hence he is not allowed to have access to protected information
such as yield. we present a tensor-based transformation for training wafers such that the
protected information is hidden.
Our automated and robust software is applied to two high reliability automotive
products, with 8300 and 7052 wafers respectively.