6 March 2018 Introducing etch kernels for efficient pattern sampling and etch bias prediction
Author Affiliations +
J. of Micro/Nanolithography, MEMS, and MOEMS, 17(1), 013505 (2018). doi:10.1117/1.JMM.17.1.013505
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—“internal, external, curvature, Gaussian, z_profile”—designed to represent the finest details of the resist geometry to characterize precisely the etch bias at any point along a resist contour. By evaluating the etch kernels on various structures, it is possible to map their etch signatures in a multidimensional space and analyze them to find an optimal sampling of structures. The etch kernels evaluated on these structures were combined with experimental etch bias derived from scanning electron microscope contours to train artificial neural networks to predict etch bias. The method applied to contact and line/space layers shows an improvement in etch model prediction accuracy over standard etch model. This work emphasizes the importance of the etch kernel definition to characterize and predict complex etch effects.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
François Weisbuch, Andrey Lutich, Jirka Schatz, "Introducing etch kernels for efficient pattern sampling and etch bias prediction," Journal of Micro/Nanolithography, MEMS, and MOEMS 17(1), 013505 (6 March 2018). https://doi.org/10.1117/1.JMM.17.1.013505 Submission: Received 22 December 2017; Accepted 6 February 2018
Submission: Received 22 December 2017; Accepted 6 February 2018


Scanning electron microscopy

Data modeling

Optical proximity correction

Optical lithography

Performance modeling

3D modeling


Back to Top