Measuring Local CD Uniformity in EUV vias with Scatterometry and Machine Learning
A methodology of obtaining the local critical dimension uniformity of contact hole arrays by using optical
scatterometry in conjunction with machine learning algorithms is presented and discussed. Staggered contact hole
arrays at 44 nm pitch were created by EUV lithography using three different positive-tone chemically amplified resists.
To introduce local critical dimension uniformity variations different exposure conditions for dose and focus were used.
Optical scatterometry spectra were acquired post development as well as post etch into a SiN layer. Reference data
for the machine learning algorithm were collected by critical dimension scanning electron microscopy (CDSEM). The
machine learning algorithm was then trained using the optical spectra and the corresponding calculated LCDU values
from CDSEM image analyses. It was found that LCDU and CD can be accurately measured with the proposed
methodology both post lithography and post etch. Additionally, since the collection of optical spectra post
development is non-destructive, same area measurements are possible to single out etch improvements. This optical
metrology technique can be readily implemented inline and significantly improves the throughput compared to
currently used electron beam measurements.
Keywords: Scatterometry, machine learning, CDSEM, hybrid metrology, EUV, LCDU