A discrete spatial model for wafer yield prediction. Issue Volume 30:Issues 2(2018) (3rd April 2018)

• Record Type: Journal Article
• Authors: Wang, Hao; Li, Bo; Tong, Seung Hoon; Chang, In-Kap; Wang, Kaibo
• Is Part Of: Quality engineering. Volume 30:Issues 2(2018)
• Page Start: 169
• Page End: 182
• Publication Date: 2018-04-03
• Subjects: Bayesian model -- generalized linear model -- spatial data -- wafer -- yield prediction; Quality control -- Periodicals; Production management -- Quality control -- Periodicals; 658.5
• DOI: 10.1080/08982112.2017.1328063
• Languages: English
• ISSNs: 0898-2112

Abstract

ABSTRACT: Yield analysis is one of the key concerns in the fabrication of semiconductor wafers. An effective yield analysis model will contribute to production planning and control, cost reductions and the enhanced competitiveness of enterprises. In this article, we propose a novel discrete spatial model based on defect data on wafer maps for analyzing and predicting wafer yields at different chip locations. More specifically, based on a Bayesian framework, we propose a hierarchical generalized linear mixed model, which incorporates both global trends and spatially correlated effects to characterize wafer yields with clustered defects. Both real and simulated data are used to validate the performance of the proposed model. The experimental results show that the newly proposed model offers an improved fit to spatially correlated wafer map data.