UC Santa Barbara

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.