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23 March 2020 Understanding and mitigating bridge defects in block copolymer directed self-assembly through computational materials design and optimization
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Block copolymers (BCPs) are appealing materials to the lithography community because of their potential to extend Moore's Law beyond the 10nm node. Not only do BCPs have the ability to microphase separate into structures such as cylinders and lamellae at single-nm length scales, but device fabricators also have control over the alignment of these structures by manipulating the energy landscape of the substrate via directed self- assembly (DSA). Despite the promise that BCPs show in offering an economical enhancement to optical lithography, the levels of defectivity in BCP-patterned devices are still above the desired levels for industrial- scale implementation. A troublesome defect mode that has been observed in experimental BCP structures is the bridge defect. Previous simulation studies by Henderson and coworkers have shown that affinity defects in chemoepitaxial underlayers have the potential to spawn bridge defects in the overlying BCP film. An important consideration in characterizing bridge defectivity is to evaluate and determine which BCP material properties are capable of inhibiting bridge propagation through a BCP film aligned atop an underlayer containing an affinity defect. In this work, coarse-grained molecular dynamics simulations were used to model BCPs with various energetic and structural properties to identify which properties impact bridge propagation. Although there was minimal correlation between BCP properties and bridge propagation, a kinetic survey showed that bridge defects generally reached their maximum thickness within the first 100ns of thermal annealing. As the BCP began undergoing long-range alignment, the bridges slowly healed before reaching an equilibrium thickness of roughly one BCP chain.
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Jakin B. Delony, Peter J. Ludovice, and Clifford L. Henderson "Understanding and mitigating bridge defects in block copolymer directed self-assembly through computational materials design and optimization", Proc. SPIE 11326, Advances in Patterning Materials and Processes XXXVII, 113261K (23 March 2020);

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