Optimizing image-based patterned defect inspection through FDTD simulations at multiple ultraviolet wavelengths
Bryan Barnes, Hui Zhou, Mark-Alexander Henn, Martin Sohn, Richard M. Silver
The sizes of non-negligible defects in the patterning of a semiconductor device continue to decrease as the dimensions for these devices are reduced. These "killer defects" disrupt the performance of the device and must be adequately controlled during manufacturing, and new solutions are required to improve optics-based defect inspection. To this end, our group has reported [Barnes et al., Proc. SPIE 1014516 (2017)] our initial five-wavelength simulation study, evaluating the extensibility of defect inspection by reducing the inspection wavelength from a deep-ultraviolet wavelength to wavelengths in the vacuum ultraviolet and the extreme ultraviolet. In that study, a 47 nm wavelength yielded enhancements in the signal to noise (SNR) by a factor of five compared to longer wavelengths and in the differential intensities by as much as three orders-of-magnitude compared to 13 nm. This paper briefly reviews these recent findings and investigates the possible sources for these disparities between results at 13 nm and 47 nm wavelengths. Our in-house finite-difference time-domain code (FDTD) is tested in both two and three dimensions to determine how computational conditions contributed to the results. A modified geometry and materials stack is presented that offers a second viewpoint of defect detectability as functions of wavelength, polarization, and defect type. Reapplication of the initial SNR-based defect metric again yields no detection of a defect at (lambda) = 13 nm, but additional image preprocessing now enables the computation of the SNR for (lambda) = 13 nm simulated images and has led to a revised defect metric that allows comparisons at all five wavelengths.
, Zhou, H.
, Henn, M.
, Sohn, M.
and Silver, R.
Optimizing image-based patterned defect inspection through FDTD simulations at multiple ultraviolet wavelengths, Proceedings of the SPIE, Munich, DE, [online], https://doi.org/10.1117/12.2271149, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923390
(Accessed December 7, 2023)