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.