Millisecond X-ray reflectometry and neural network analysis: unveiling fast processes in spin coating

Area of Science:

• Nanotechnology and Materials Science
• Surface Science and Thin Film Analysis

Background:

• X-ray Reflectometry (XRR) is crucial for characterizing nanoscale films and layered structures in semiconductor and optics manufacturing.
• Existing XRR methods are often too slow for real-time monitoring of rapid nanoscale processes.

Purpose of the Study:

• To develop and demonstrate a novel, high-speed XRR technique for quantitative, millisecond-scale measurements.
• To enable real-time, in situ monitoring of fast nanoscale processes, such as thin film formation.

Main Methods:

• Development of a novel millisecond monochromatic X-ray Reflectometry (XRR) system, termed Quick XRR (qXRR).
• Demonstration of qXRR with a record acquisition time of 1.4 ms for static gold thin films.
• Application of qXRR for dynamic in situ measurements during PMMA spin coating, coupled with machine learning for rapid data fitting.

Main Results:

• Achieved millisecond acquisition times, an order of magnitude faster than previous methods.
• Successfully monitored dynamic in situ processes like thin film formation during spin coating.
• Resolved initial film thinning via mass transport and later thinning via solvent evaporation.

Conclusions:

• Millisecond qXRR is a significant advancement for in situ studies of fast thin film deposition processes.
• The technique can track intrinsically fast phenomena like high deposition rate growth and spin coating.
• qXRR has broad implications for studying rapid structural changes, including photostriction and diffusion.