Predictive discarding of wafers based on power leakage predictions from single layer misalignment data

Photolithography is a process used in the manufacturing of dies, which are at the core of complex integrated circuits. During this process several layers of semi-conducting material are stacked on top of each other. Precise alignment of the layers is crucial to the performance of a die. Upon completion, each die is subjected to several electrical tests. If many dies of a wafer fail the test, the whole wafer is considered faulty and has to be discarded, or reworked). This paper proposes the use of machine learning models to predict the outcome of a crucial test for MOS power leakage, from misalignments of a single layer. Wafers which are predicted to be faulty when finished can be predictively discarded, saving costs and resources otherwise spent on finishing the faulty wafer.