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.

翻译：在制造业中，返工是指生产过程中可选环节，旨在消除误差或修复未达到预期质量标准的缺陷品。对生产批次进行返工需要重复前一道生产工序并做调整，以确保最终产品符合规定规格。返工虽有机会提升良率从而增加生产批次收益，但也会产生额外成本。此外，对已满足目标规格的零件进行返工可能造成损坏并降低良率。本文应用双重/去偏机器学习（DML）方法，估计光电子半导体制造中色彩转换工序的返工环节对最终产品良率的条件处理效应。我们运用DoubleML工具包制定零件返工策略，并通过实证评估其价值。基于因果机器学习分析，我们推导出对涂覆转换层的单色发光二极管（LED）工艺的启示。