Translator Disclaimer
24 March 2017 Optimal structure sampling for etch model calibration
Author Affiliations +
Successful patterning requires good control of the photolithography and etch processes. While compact litho models, mainly based on rigorous physics, can predict very well the contours printed in photoresist, pure empirical etch models are less accurate and more unstable. Compact etch models are based on geometrical kernels to compute the litho-etch biases that measure the distance between litho and etch contours. The definition of the kernels as well as the choice of calibration patterns is critical to get a robust etch model. This work proposes to define a set of independent and anisotropic etch kernels designed to capture the finest details of the resist contours and represent precisely any etch bias. By evaluating the etch kernels on various structures it is possible to map their etch signatures in a multi-dimensional space and analyze them to find an optimal sampling of structures to train an etch model. The method was specifically applied to a contact layer containing many different geometries and was used to successfully select appropriate calibration structures. The proposed kernels evaluated on these structures were combined to train an etch model significantly better than the standard one.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
François Weisbuch, Andrey Lutich, and Jirka Schatz "Optimal structure sampling for etch model calibration", Proc. SPIE 10147, Optical Microlithography XXX, 101470I (24 March 2017);


Calibrating etch model with SEM contours
Proceedings of SPIE (March 18 2015)
Pattern sampling for etch model calibration
Proceedings of SPIE (September 28 2017)
Resist loss in 3D compact modeling
Proceedings of SPIE (March 13 2012)

Back to Top