Optimization of multiple quality responses involving qualitative and quantitative characteristics in IC manufacturing using neural networks

Abstract The optimization of product or process quality profoundly influences a manufacturer. Most studies have focused primarily on optimizing a quantitative (or qualitative) quality response, while others have concentrated on optimizing multiple quantitative quality responses. However, optimizing multiple responses involving both qualitative and quantitative characteristics have scarcely been mentioned, largely owing to the inability to directly apply conventional optimization techniques. In this study, we present a novel approach based on artificial neural networks (ANNs) to simultaneously optimize multiple responses including both qualitative and quantitative quality characteristics. Two neural networks are constructed: one for determining the ideal parameter settings and the other for estimating the values of the multiple quality characteristics. In addition, a numerical example from an ion implantation process employed by a Taiwan IC fabrication manufacturer demonstrates the proposed approach’s effectiveness.

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