Discovering Interesting Plots in Production Yield Data Analytics

TL;DR

This paper presents a method to automate the identification of interesting plots in production yield data analytics by learning analyst preferences using GANs, aiming to enhance yield optimization processes.

Contribution

It introduces a novel approach leveraging GANs to learn and automate the evaluation of interesting plots, reducing reliance on domain knowledge.

Findings

Successfully applied to production yield data from multiple product lines

Demonstrated ability to identify plots aligned with analyst interests

Improved efficiency in data analysis process

Abstract

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.