  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.

Machine Learning;Artificial Intelligence;Machine Learning