Value proposition of predictive discarding in semiconductor manufacturing

Abstract

Optimizing semiconductor manufacturing processes is needed to solve the current shortage of computer chips. Discarding unfinished chips based on data-driven predictions models can significantly reduce time and resources otherwise spent on finishing faulty chips. The current paper presents the value proposition of predictive discarding at different stages in the manufacturing process, by combining model performance metrics with costs and benefits related to false and correct discards. While applied to the chip manufacturing process in this paper, predictive discarding is a generic methodology to minimize wasted resources by predicting product quality from process data. Through sensitivity analysis, we show that even with weak predictors, predictive discarding can still be beneficial, from both economic and sustainability perspectives. The proposed method is illustrated by analysing an empirical benchmark data set from the semiconductor manufacturing domain.