This paper focuses on orthogonal model corrections where model parameters do not influence each other as long as the
measurement layout is sufficiently symmetric. For the grid correction we used Zernike polynomials, and for the intrafield
correction we used a two-dimensional set of Legendre polynomials. We enabled these corrections by developing a
transformation matrix as an exposure tool is incapable of correcting such orthogonal polynomials. Simulation with
OVALiS shows that the linear parameters get stabilized by several factors when using a combined Zernike/Legendre
model. The correlation between linear and higher order parameters disappears, and overlay mean plus 3-sigma improves
up to ~15–20%. Simulated data agrees well with experimental and electrical data. Additionally, we introduced an
interpolated metric that probed the wafer and field with a dense grid. This interpolated metric showed that the
Zernike/Legendre model-based correction does not cause over-correction like that seen on standard polynomial models.
We have tested higher order process corrections comprehensively by enabling an orthogonal model, as well as by
making use of interpolated metrics to monitor the overlay performance. These orthogonal models can be implemented in
the production line based on inline overlay data where interpolated metrics will ensure that there is no over-correction
and no negative impact on product.