A hierarchical model for characterising spatial wafer variations

A Hierarchical Model for Characterizing Spatial Wafer Variations

Akrng wafer axis Hierarchical Modeling of Spatial Variability with a 45nm Example

In previous publications we have proposed a hierarchical variability model and verified it with 90nm test data. This model is now validated with a new set of 45nm test chips. A mixed sampling scheme with both sparse and exhaustive measurements is designed to capture both wafer level and chip level variations. Statistical analysis shows that the acrosswafer systematic function can be sufficientl...

A Hierarchical Max-stable Spatial Model for Extreme Precipitation1

Extreme environmental phenomena such as major precipitation events manifestly exhibit spatial dependence. Max-stable processes are a class of asymptotically-justified models that are capable of representing spatial dependence among extreme values. While these models satisfy modeling requirements, they are limited in their utility because their corresponding joint likelihoods are unknown for mor...

A Hierarchical Max-stable Spatial Model for Extreme Precipitation.

Extreme environmental phenomena such as major precipitation events manifestly exhibit spatial dependence. Max-stable processes are a class of asymptotically-justified models that are capable of representing spatial dependence among extreme values. While these models satisfy modeling requirements, they are limited in their utility because their corresponding joint likelihoods are unknown for mor...

A hierarchical model for spatial capture-recapture data.

Estimating density is a fundamental objective of many animal population studies. Application of methods for estimating population size from ostensibly closed populations is widespread, but ineffective for estimating absolute density because most populations are subject to short-term movements or so-called temporary emigration. This phenomenon invalidates the resulting estimates because the effe...