TY - GEN
T1 - Dictionary based image enhancement for integrated circuit imaging
AU - Cilingiroglu, T. B.
AU - Tuysuzoglu, A.
AU - Karl, W. C.
AU - Konrad, J.
AU - Unlu, M. S.
AU - Goldberg, B. B.
PY - 2013/10/18
Y1 - 2013/10/18
N2 - The rapid decrease in the dimensions of integrated circuits has necessitated corresponding higher-resolution methods for defect imaging. Current state of the art, defect imaging systems are reaching the limits of their resolution. In this work, we are proposing a new overcomplete dictionary based sparse signal imaging framework to improve the resolution and localization in confocal microscopy systems for backside optical integrated circuit defect imaging. The domain of integrated circuit imaging is particularly suitable for the application of overcomplete dictionaries in an image reconstruction framework because the images are highly structured, containing predictable building blocks derivable from the corresponding computer aided design layouts. This structure provides a strong and natural a-priori dictionary for scene reconstruction. This dictionary prior is coupled with a physically-based observation model to create enhanced scene reconstructions. The approach is described and results on simulated data are provided.
AB - The rapid decrease in the dimensions of integrated circuits has necessitated corresponding higher-resolution methods for defect imaging. Current state of the art, defect imaging systems are reaching the limits of their resolution. In this work, we are proposing a new overcomplete dictionary based sparse signal imaging framework to improve the resolution and localization in confocal microscopy systems for backside optical integrated circuit defect imaging. The domain of integrated circuit imaging is particularly suitable for the application of overcomplete dictionaries in an image reconstruction framework because the images are highly structured, containing predictable building blocks derivable from the corresponding computer aided design layouts. This structure provides a strong and natural a-priori dictionary for scene reconstruction. This dictionary prior is coupled with a physically-based observation model to create enhanced scene reconstructions. The approach is described and results on simulated data are provided.
KW - backside integrated circuit imaging
KW - high numerical aperture microscopy
KW - image reconstruction
KW - overcomplete dictionary
KW - sparse signal representation
UR - http://www.scopus.com/inward/record.url?scp=84890523076&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890523076&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6637977
DO - 10.1109/ICASSP.2013.6637977
M3 - Conference contribution
AN - SCOPUS:84890523076
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1869
EP - 1873
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -