Enhancing metrology by combining spatial variability and global inference

Abstract

Recently, there has been significant interest in so-called Hybrid or Holistic Metrology, the practice of combining measurements from multiple sources in order to improve the estimation of one or more critical parameters. There also has been significant research in capturing and modeling the hierarchical spatial variability of CDs at the die, wafer, and lot level. However, the information inherent in spatial variability models has not been used towards improving the accuracy/precision of CD estimates. In this paper, we review the current trends in Hybrid Metrology and Spatial Variability Modeling, and provide a simple example based on the work of Zhang et al. 1 that illustrates how we can incorporate spatial information for improved measurement estimates.

Optimizing hybrid metrology through a consistent multi-tool parameter set and uncertainty model

“…This method provides a technique to maximize the utility of SEM or CD-SAXS measurement data by weighing the utility of more accurate high-resolution information versus additional acquisition and analysis time. The method is also well suited to include manufacturing variability into the hybrid model from predictable effects such as process induced variation due to a bake plate or known lithography variability [9].…”

“…Hybrid metrology has gained significant recognition in a short period of time as an approach to reduce overall measurement uncertainty and optimize measurement throughput while having the potential to yield more complete information [1][2][3][4][5][6][7][8][9]. The method allows for rigorous combinations of two or more different measurement techniques into a single result.…”