Discovering Interesting Plots in Production Yield Data Analytics
This work addresses the challenge of reducing manual effort in data evaluation for analysts in manufacturing or production domains, but it is incremental as it builds on existing GAN methods for a specific step in analytics.
The paper tackled the problem of automating the identification of interesting plots in production yield data analytics by learning analyst intent using Generative Adversarial Networks (GANs), and demonstrated its application in optimizing production yield across multiple product lines.
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.