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Critical aspect ratio induced pattern collapse has been a concern for lithography process engineers since
before the 180 nm node. This has driven a steady reduction in photoresist thickness, as well as accelerated
the introduction of hard mask materials and processes. There have been multiple studies and proposed
models for the fundamental forces which lead to pattern collapse, with consensus being that differential
capillary forces across opposing sides of a developed photoresist line during the water rinse/dry step are
responsible for line bending. This line bending can lead to pattern deformation or complete substrate
adhesion failure. Several process improvements, such as surfactant-laced final rinse, have been proposed to
alter surface energies and increase the critical aspect ratio for collapse. The mechanical models which have
been proposed to explain this phenomenon have all been effectively two-dimensional, characterizing the
aspect ratio (Z/X) for an idealized pattern assumed to be semi-infinite in the Y dimension. While these
models are very helpful for guiding materials/process understanding and improvement, they are not as
helpful in predicting real random logic "hotspots": pattern locations most likely to fail through the
manufacturing process window. This work proposes a framework for full-chip prediction of pattern
collapse failure by use of a semi-empirical model and an efficient geometric checking engine. Idealized test
patterns are studied initially, in order to reproduce behaviors commonly reported in the literature for semiinfinite
Y patterns, and then more complex layout configurations are introduced. It is shown that full-chip
pattern collapse can be efficiently and accurately predicted. Such a capability can prove valuable in setting
design rules, and in refining RET solutions.
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