Displacement sensing and estimation (DSE) is important preprocessing task for many image-based processing systems
that extract information from multiple images. In last two years, we gained significant insight of the nature of DSE and
developed theory and algorithm framework named nanoscale displacement sensing and estimation (nDSE). We also
build procedures to apply nDSE to overlay alignment down to the nanoscale. We will introduce two basic theories:
Phase Delay Detection (PDD) and Derivatives-based Maximum Likelihood Estimation (DML) and associated DSE
algorithms, noticeably Near-Neighbor-Navigation (N-Cubed) algorithm. We presented our best nDSE experimental
result of 1 nm (1σ) while tracking 5 nm stepping. To develop nDSE-based nanoscale alignment, we introduced our
definition of displacement, alignment and pseudo-displacement. We presented both theoretical and practical procedures
to use nDSE to achieve nano-alignment down to the 10s of nano-meters and beyond. Then we compared nDSE-based
nano-alignment to other industry standard alignment method and attempt to show the substantial advantages of nDSE
based alignment in terms of cost and simplicity of the system design.
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