Qualification of structured curvilinear ILT assist features for production
Source abstract
Abstract
BackgroundThe original inverse lithography technology (ILT) commitment of creating a free-form mask from ex nihilo underwent transformation under the pressure of constraints that are imposed by the capabilities of manufacturing and inspection equipment. This pressure became especially noticeable in dealing with sub-resolution assist features (SRAF). The free-form SRAFs are naturally unmanufacturable and hard to inspect. In addition to this complication, the ILT signal for SRAFs is substantially weaker and often ill-defined in comparison to a strong signal from fidelity objectives to print main features on target. So, it is difficult to automatically produce SRAFs that are simultaneously geometrically stable, do not print, MRC-clean, and deliver the best lithographic quality.AimTo overcome these obstacles, we propose new concept of structured SRAFs by combining ILT ideas with robust geometrical parameterization to introduce well-behaved and MRC-clean by construction mask decorations.ApproachWe implemented insertion of structured assist features within full-chip calibre ILT framework to probe for runtime, lithographical quality, and MRC compliance.ResultsRuntime is estimated to be 2.3 times faster than the native ILT SRAFs, without the necessity of using post-ILT MRC cleanup. The lithographic quality, as quantified by the process variability bands, was comparable to the native ILT solution (19% difference) and was 43% better than the rule-based Manhattan SRAFs (1.22 nm versus 1.75 nm).ConclusionsWe proposed structured assist features by combining ILT signal and vector field ideas to introduce well-behaved and MRC-clean by construction mask decorations. This technique compares favorably to other SRAF methods by consistency, lithographic quality, and runtime metrics.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Siemens Digital Industries Software, Fremont, California, United States
References (9)
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