Hotspot-aware DSA Grouping and Mask Assignment
Yasmine Badr, Puneet Gupta

TL;DR
This paper introduces a heuristic method for DSA grouping and mask assignment that significantly reduces hotspots and conflicts in directed self-assembly processes, improving yield and reliability in semiconductor manufacturing.
Contribution
A novel hotspot-aware heuristic for DSA grouping and MP decomposition that reduces hotspots by 78% compared to hotspot-unaware methods.
Findings
Eliminates 78% of hotspots and conflicts
Results are within 24% of the optimal ILP solution
Improves defect reduction in DSA processes
Abstract
In Directed Self Assembly (DSA), poor printing of guiding templates can cause misassembly resulting in high defect probability. Therefore, hotspots should be avoided in the choice of the DSA groups. Accordingly, Directed Self-Assembly (DSA) technologies which use Multiple Patterning (MP) to print the guiding templates need to be aware of hotspots during the DSA grouping and MP Decomposition. In this paper, we present a hotspot-aware heuristic for DSA grouping and MP decomposition. Results show that that the proposed heuristic eliminates 78% of the hotspots and conflicts that result from using a hotspot-unaware grouping and decomposition algorithm. In comparison to the optimal solution using Integer Linear Programming, the proposed heuristic results in ~24% more violations.
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Taxonomy
TopicsModular Robots and Swarm Intelligence · Advancements in Photolithography Techniques · 3D IC and TSV technologies
