SCHUMI-MAREČEK, David, Florian BERTRAM, Petr MIKULÍK, Devanshu VARSHNEY, Jiří NOVÁK and Stefan KOWARIK. Millisecond X-ray reflectometry and neural network analysis: unveiling fast processes in spin coating. Journal of Applied Crystallography. International Union of Crystallography, 2024, vol. 57, No 2, p. 314-323. ISSN 1600-5767. Available from: https://dx.doi.org/10.1107/S1600576724001171.
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Basic information
Original name Millisecond X-ray reflectometry and neural network analysis: unveiling fast processes in spin coating
Authors SCHUMI-MAREČEK, David, Florian BERTRAM, Petr MIKULÍK, Devanshu VARSHNEY, Jiří NOVÁK and Stefan KOWARIK.
Edition Journal of Applied Crystallography, International Union of Crystallography, 2024, 1600-5767.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10302 Condensed matter physics
Country of publisher United Kingdom of Great Britain and Northern Ireland
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 6.100 in 2022
Organization unit Faculty of Science
Doi http://dx.doi.org/10.1107/S1600576724001171
Keywords in English millisecond XRR; neural network analysis; spin coating; X-ray reflectometry; X-ray reflectometry
Tags International impact, Reviewed
Changed by Changed by: Mgr. Marie Šípková, DiS., učo 437722. Changed: 17/4/2024 14:27.
Abstract
X-ray reflectometry (XRR) is a powerful tool for probing the structural characteristics of nanoscale films and layered structures, which is an important field of nanotechnology and is often used in semiconductor and optics manufacturing. This study introduces a novel approach for conducting quantitative high-resolution millisecond monochromatic XRR measurements. This is an order of magnitude faster than in previously published work. Quick XRR (qXRR) enables real time and in situ monitoring of nanoscale processes such as thin film formation during spin coating. A record qXRR acquisition time of 1.4 ms is demonstrated for a static gold thin film on a silicon sample. As a second example of this novel approach, dynamic in situ measurements are performed during PMMA spin coating onto silicon wafers and fast fitting of XRR curves using machine learning is demonstrated. This investigation primarily focuses on the evolution of film structure and surface morphology, resolving for the first time with qXRR the initial film thinning via mass transport and also shedding light on later thinning via solvent evaporation. This innovative millisecond qXRR technique is of significance for in situ studies of thin film deposition. It addresses the challenge of following intrinsically fast processes, such as thin film growth of high deposition rate or spin coating. Beyond thin film growth processes, millisecond XRR has implications for resolving fast structural changes such as photostriction or diffusion processes.
Links
EH22_008/0004572, research and development projectName: Kvantové materiály pro aplikace v udržitelných technologiích
GA22-04551S, research and development projectName: Růst organických polovodičů na grafenu: od vzniku první monovrstvy k molekulárním multivrstvám (Acronym: GOSG)
Investor: Czech Science Foundation
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