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Related Experiment Video

Updated: Jun 5, 2025

Characterization Of Multi-layered Fish Scales Atractosteus spatula Using Nanoindentation, X-ray CT, FTIR, and SEM

Characterization Of Multi-layered Fish Scales Atractosteus spatula Using Nanoindentation, X-ray CT, FTIR, and SEM

Published on: July 10, 2014

Generic characterization method for nano-gratings using deep-neural-network-assisted ellipsometry.

Zijie Jiang¹, Zhuofei Gan¹, Chuwei Liang¹

¹Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China.

Nanophotonics (Berlin, Germany)

|December 5, 2024

Summary

This summary is machine-generated.

Deep-neural-network-assisted ellipsometry offers a stable and accurate method for characterizing nano-gratings. This novel approach overcomes limitations of optical scatterometry for nanostructure analysis.

Keywords:

deep neural network in-situ measurement interference lithography nanoimprint lithography reactive-ion etching spectroscopic ellipsometry

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Area of Science:

Nanotechnology
Optical Physics
Materials Science

Background:

Optical scatterometry is a non-destructive technique for measuring film thickness and optical constants.
Deep learning advances offer new solutions for inverse scattering problems.
Current deep-neural-network-assisted optical scatterometry for nanostructures faces challenges in stability, functionality, and equipment demands.

Purpose of the Study:

To propose a novel characterization method using deep-neural-network-assisted ellipsometry for nanostructures.
To address the limitations of existing optical scatterometry techniques for nano-gratings.
To develop a stable, accurate, and efficient method for nano-grating characterization.

Main Methods:

Utilized ellipsometric angles measured by basic ellipsometers as functional signals.
Developed a comprehensive model incorporating rounded corners, residual layers, and optical constants for nano-grating profiling.
Enhanced model stability through multiple initial values and azimuth-resolved measurements.
Implemented a compensation algorithm to improve accuracy without sacrificing efficiency.

Main Results:

The proposed method accurately characterizes nano-gratings fabricated by diverse techniques.
Relative errors for both geometric and optical parameters were controlled under 5%.
Demonstrated rapid and accurate performance in experimental validation.

Conclusions:

Deep-neural-network-assisted ellipsometry provides a promising alternative to conventional characterization techniques.
The method is suitable for in-situ measurement of nano-gratings.
Overcame challenges associated with stability, functionality, and equipment requirements in previous methods.