Metrology-design co-optimization for BEOL dimensional characterization using scatterometry

A systematic study of the co-optimization of target design and metrology technique is presented to accurately measure the critical dimensions of backend of line (BEOL) metal line gratings. Rigorous coupled-wave analysis calculations and machine learning approaches are combined to evaluate various design scenarios with and without patterned underlayers in conjunction with either traditional scatterometry or vertical traveling scatterometry (VTS) using spectral interferometry. It was found that for traditional scatterometry techniques employing polarized reflectometry or ellipsometry, two levels of crossed metal lines buried below the level of interest are often sufficient to suppress most of the optical contributions from any underlayer stack beneath. Alternatively, VTS utilizing spectral interferometry and signal filtering can suppress all contributions from the underlayer stack independent of the design choice thus only the top layer of interest needs to be considered in the model analysis. Machine learning models trained on VTS data instead of traditional scatterometry data can improve the accuracy and ease of setup, for example, by utilizing simplified targets for training. Three relevant BEOL cases for measurements after an etch step, a polishing step, and dielectric layer deposition on patterned metal lines are addressed.