A compressed learning framework claims to classify five levels of deficiency in ZnO nanosurfaces from 1 to 6 percent of angle-resolved scatterometry samples, with 86 to 94 percent accuracy.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SP 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Compressed Learning for Nanosurface Deficiency Recognition Using Angle-resolved Scatterometry Data
A compressed learning framework claims to classify five levels of deficiency in ZnO nanosurfaces from 1 to 6 percent of angle-resolved scatterometry samples, with 86 to 94 percent accuracy.