Causally Learning an Optimal Rework Policy

TL;DR

This paper employs causal machine learning to estimate the effects of rework policies in manufacturing, specifically optimizing rework decisions to improve yield while minimizing costs and damage.

Contribution

It introduces a novel application of double/debiased machine learning to derive optimal rework policies in semiconductor manufacturing.

Findings

Rework policies can significantly impact final product yield.

Causal analysis informs cost-effective rework strategies.

Empirical estimates demonstrate policy improvements.

Abstract

In manufacturing, rework refers to an optional step of a production process which aims to eliminate errors or remedy products that do not meet the desired quality standards. Reworking a production lot involves repeating a previous production stage with adjustments to ensure that the final product meets the required specifications. While offering the chance to improve the yield and thus increase the revenue of a production lot, a rework step also incurs additional costs. Additionally, the rework of parts that already meet the target specifications may damage them and decrease the yield. In this paper, we apply double/debiased machine learning (DML) to estimate the conditional treatment effect of a rework step during the color conversion process in opto-electronic semiconductor manufacturing on the final product yield. We utilize the implementation DoubleML to develop policies for the rework of components and estimate their value empirically. From our causal machine learning analysis we derive implications for the coating of monochromatic LEDs with conversion layers.