Multi-Target Regression via Target Combinations using PCA with application to quality prediction in OLED manufacturing process

This study focuses on improving quality prediction in Organic Light Emitting Diode (OLED) manufacturing using multi-target regression (MTR), which is called the MTR via Target Combinations Using Principal Component Analysis method (PCA-C). OLED is a complex device, fabricated by stacking multiple layers of light-emitting materials, but the production quality is difficult to control. The PCA-C method is designed to enhance prediction accuracy by considering relationships among multiple quality inspection variables, such as CIE-x, CIE-y, luminance, Ra, and R9, which define color and luminance attributes of OLED panels. This method also utilizes a bagging technique. Our experiments compare PCA-C with conventional single-target regressors (STR), such as gradient boosting (GB) and random forest (RF), and PLS2, a conventional MTR method, revealing improvements in accuracy across a variety of conditions, from small to large training data. These improvements were observed across all target variables. PCA-C based on GB often showed the highest accuracy. Compared to the conventional GB, the accuracy has been improved by 10 %–22 %. PCA-C based on RF has also improved accuracy by 7 %–112 % compared to conventional RF. Moreover, comparative experiments between PCA-C and its base model confirmed that the improvement in the method's accuracy resulted from the effects of the bagging and relationship elements.