20 March 2018 Modeling of block copolymer dry etching for directed self-assembly lithography
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Abstract
Directed self-assembly (DSA) of block copolymers (BCP) is a promising alternative technology to overcome the limits of patterning for the semiconductor industry. DSA exploits the self-assembling property of BCPs for nano-scale manufacturing and to repair defects in patterns created during photolithography. After self-assembly of BCPs, to transfer the created pattern to the underlying substrate, selective etching of PMMA (poly (methyl methacrylate)) to PS (polystyrene) is required. However, the etch process to transfer the self-assemble “fingerprint” DSA patterns to the underlying layer is still a challenge. Using combined experimental and modelling studies increases understanding of plasma interaction with BCP materials during the etch process and supports the development of selective process that form well-defined patterns. In this paper, a simple model based on a generic surface model has been developed and an investigation to understand the etch behavior of PS-b-PMMA for Ar, and Ar/O2 plasma chemistries has been conducted. The implemented model is calibrated for etch rates and etch profiles with literature data to extract parameters and conduct simulations. In order to understand the effect of the plasma on the block copolymers, first the etch model was calibrated for polystyrene (PS) and poly (methyl methacrylate) (PMMA) homopolymers. After calibration of the model with the homopolymers etch rate, a full Monte-Carlo simulation was conducted and simulation results are compared with the critical-dimension (CD) and selectivity of etch profile measurement. In addition, etch simulations for lamellae pattern have been demonstrated, using the implemented model.
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Zelalem Belete, Zelalem Belete, Eberhard Baer, Eberhard Baer, Andreas Erdmann, Andreas Erdmann, } "Modeling of block copolymer dry etching for directed self-assembly lithography", Proc. SPIE 10589, Advanced Etch Technology for Nanopatterning VII, 105890U (20 March 2018); doi: 10.1117/12.2299977; https://doi.org/10.1117/12.2299977
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