Design principle of super resolution near-field structure using thermally responsive optical phase change materials for nanolithography applications
- Authors
- Park, Gwanwoo; Lee, Jinhyung; Kang, Sunggu; Kim, Minsoo; Kang, Shinill; Choi, Wonjoon
- Issue Date
- 15-7월-2016
- Publisher
- ELSEVIER SCI LTD
- Keywords
- Maskless nanolithography; Phase change material; Super-resolution near-field structure (Super-RENS); Photo-thermal simulation
- Citation
- MATERIALS & DESIGN, v.102, pp.45 - 55
- Indexed
- SCIE
SCOPUS
- Journal Title
- MATERIALS & DESIGN
- Volume
- 102
- Start Page
- 45
- End Page
- 55
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/88061
- DOI
- 10.1016/j.matdes.2016.04.026
- ISSN
- 0264-1275
- Abstract
- The super resolution near-field structure (Super-RENS) which is composed of a thin layer of a thermally responsive optical phase change material (PCM) between two dielectric layers, can be a means of resolving the limited resolution of the laser beam for direct laser lithography. In Super-RENS, incident laser irradiation induces the direct, reversible opening and closing of a nanoaperture in the PCM layer, and a nanoscale pattern is realized in the lithography system. Here, we first introduce the complete modeling procedures and optimization methodology for Super-RENS in nanolithography based on a rigorous analysis of near-field structure, thermal analysis in the finite-element method, and analysis of the corresponding feature size on the photoresist (PR) layer. Multiple combinations of the PCM layer and the two dielectric layers with varying dimensions are considered as design parameters to achieve the required resolution in the nanolithography system. The feasible line profiles are investigated at the general operating conditions of the pulsed laser beam, based on varying dimensions of the PCM layer (5-30 nm) and the two dielectric layers (10-200 nm). This work will provide a detailed methodology for the design and optimization of the Super-RENS for applications in the nanolithography system. (C) 2016 Elsevier Ltd. All rights reserved.
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