1 research outputs found
Discovering Interesting Plots in Production Yield Data Analytics
An analytic process is iterative between two agents, an analyst and an
analytic toolbox. Each iteration comprises three main steps: preparing a
dataset, running an analytic tool, and evaluating the result, where dataset
preparation and result evaluation, conducted by the analyst, are largely
domain-knowledge driven. In this work, the focus is on automating the result
evaluation step. The underlying problem is to identify plots that are deemed
interesting by an analyst. We propose a methodology to learn such analyst's
intent based on Generative Adversarial Networks (GANs) and demonstrate its
applications in the context of production yield optimization using data
collected from several product lines