wlzpoly is a Python package that decomposes N-point wafer thickness measurements into M Zernike polynomial coefficients using LSQ or Ridge regression with LOOCV-tuned regularization. It ships with a reproducible three-stage demo (synthetic data, coefficient fitting, ground-truth verification), per-stage CLI, and a clean pure-Python API for semiconductor wafer-level process metrology.
This report surveys Polynomial Machine Learning (PML) at an introductory level. PML refers to the family of techniques that exploit higher-order and interaction terms of input variables to learn nonlinear relationships. The discussion is organized along three taxonomic axes and arranged into six hierarchical levels. Deep mathematical or theoretical analysis is intentionally avoided; the goal…
Thickness uniformity in thin-film deposition determines downstream yield and device performance. Variation arises along two distinct axes — within a single wafer (Within-Wafer, WiW) and across wafers over time (Wafer-to-Wafer, W2W). These two axes have different physical origins and demand different diagnostic treatments. Mixing them into a single ML target forces the model to learn…
