A Wafer Map Yield Prediction Based on Machine Learning for Productivity Enhancement

Abstract

Manufacturing productivity in the semiconductor industry is a key factor in determining the competitiveness of manufacturers. In order to enhance productivity, evaluating the productivity of wafer maps prior to production and optimizing the productivity of wafer maps is one of the most effective solutions. The productivity of a wafer map is evaluated in advance by considering various factors affecting wafer productivity such as: gross dies, shot counts, lithography throughputs, mask field occupancy (MFO), prices, etc. Manufacturing process information is not determined at the initial wafer map design stage. Predicting the yield of new wafer maps before fabrication is a difficult challenge due to lack of process information. However, a yield prediction model is required to precisely evaluate the productivity of new wafer maps, because the yield is directly related to the productivity and the design of wafer map affects the yield. In this paper, we propose a novel yield prediction model based on deep learning algorithms. Our approach exploits spatial relationships among positions of dies on a wafer and die-level yield variations collected from a wafer test without process parameters. By modeling these spatial features, the accuracy of yield prediction significantly increased. Furthermore, experimental results showed that the proposed yield model and approach helps to design wafer maps with up to 8.59% higher productivity.