Metrology Sampling Strategies for Process Monitoring Applications

Vincent, Tyrone L.; Stirton, J. Broc; Poolla, Kameshwar

doi:10.1109/tsm.2011.2159139

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…Vincent et al 5 explores the use of modeling and minimum-variance prediction as a method to select sites for measurement on each wafer by using statistical tools such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA). The within-wafer spatial variability information is captured in the principal components obtained by analyzing a historical set of data.…”

Section: Interpolating Metrologymentioning

confidence: 99%

Enhancing metrology by combining spatial variability and global inference

Spanos

Baek

2013

SPIE Proceedings

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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.

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Section: Interpolating Metrologymentioning

confidence: 99%

Enhancing metrology by combining spatial variability and global inference

Spanos

Baek

2013

SPIE Proceedings

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“…Ideally each wafer should be measured at a large number of locations to provide detailed performance information for APC, and also for Predictive Maintenance (PdM) and product quality assessment activities [10], [25], [26], [29]. In practice, such extensive metrology is not feasible due to the impact on cycle-time and the high cost of the precision metrology technologies needed [24].…”

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confidence: 99%

“…In recent years there has been increasing interest in developing data driven wafer measurement plan optimization methodologies that can take account of spatial correlation to further reduce the number of sites that need to be measured. Vincent et al [25] developed a methodology based on Principal Component Analysis (PCA) modelling and minimumvariance estimation. They considered both within wafer spatial patterns and temporal correlation patterns in their formulation, but concluded that only spatial patterns were present in the practical lithoetch process case study they used to validate their approach.…”

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confidence: 99%

A Methodology for Efficient Dynamic Spatial Sampling and Reconstruction of Wafer Profiles

Susto¹

2020

Preprint

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<div>In semiconductor manufacturing, metrology is generally</div><div>a high cost, non-value added operation that impacts</div><div>significantly on cycle time. As such, reducing wafer</div><div>metrology continues to be a major target in semiconductor</div><div>manufacturing efficiency initiatives. A novel</div><div>data-driven spatial dynamic sampling methodology is</div><div>presented that minimises the number of sites that need</div><div>to be measured across a wafer surface while maintaining</div><div>an acceptable level of wafer profile reconstruction</div><div>accuracy. The methodology is based on analysing historical</div><div>metrology data using Forward Selection Component</div><div>Analysis (FSCA) to determine, from a set of candidate wafer sites, the minimum set of sites that</div><div>need to be monitored in order to reconstruct the full</div><div>wafer profile using statistical regression techniques.</div><div>Dynamic sampling is then implemented by clustering</div><div>unmeasured sites in accordance with their similarity</div><div>to the FSCA selected sites, and temporally selecting a</div><div>different sample from each cluster. In this way, the risk</div><div>of not detecting previously unseen process behaviour</div><div>is mitigated. We demonstrate the efficacy of the proposed</div><div>methodology using both simulation studies and</div><div>metrology data from a semiconductor manufacturing</div><div>process.</div>

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confidence: 99%

“…Vincent et al [25] developed a methodology based on Principal Component Analysis (PCA) modelling and minimum-variance estimation. They considered both within wafer spatial patterns and temporal correlation patterns in their formulation, but concluded that only spatial patterns were present in the practical litho-etch process case study they used to validate their approach.…”

mentioning

confidence: 99%