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

2020-04-27T18:05:48Z (GMT) by Gian Antonio Susto
In semiconductor manufacturing, metrology is generally
a high cost, non-value added operation that impacts
significantly on cycle time. As such, reducing wafer
metrology continues to be a major target in semiconductor
manufacturing efficiency initiatives. A novel
data-driven spatial dynamic sampling methodology is
presented that minimises the number of sites that need
to be measured across a wafer surface while maintaining
an acceptable level of wafer profile reconstruction
accuracy. The methodology is based on analysing historical
metrology data using Forward Selection Component
Analysis (FSCA) to determine, from a set of candidate wafer sites, the minimum set of sites that
need to be monitored in order to reconstruct the full
wafer profile using statistical regression techniques.
Dynamic sampling is then implemented by clustering
unmeasured sites in accordance with their similarity
to the FSCA selected sites, and temporally selecting a
different sample from each cluster. In this way, the risk
of not detecting previously unseen process behaviour
is mitigated. We demonstrate the efficacy of the proposed
methodology using both simulation studies and
metrology data from a semiconductor manufacturing
process.