Thin-film thickness measurement based on spectral reflectometer using artificial neural network algorithm

Jonghan Jin; Joonyoung Lee

doi:10.1117/12.2673342

15 August 2023 Thin-film thickness measurement based on spectral reflectometer using artificial neural network algorithm

Jonghan Jin, Joonyoung Lee

Author Affiliations +

Proceedings Volume 12618, Optical Measurement Systems for Industrial Inspection XIII; 1261804 (2023) https://doi.org/10.1117/12.2673342
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

Thin films have been widely used in advanced high-technology manufacturing processes such as semiconductors, displays, and batteries. A representative method among non-destructive thickness measurement methods is a spectral reflectometer. An optical layout of the spectral reflectometer is compact with only a few optical components, and a mathematical model for analysis is also relatively simple. However, in order to find a thickness solution based on the model-based analysis, an initial value should be well chosen to cover. In addition, the analysis time takes longer for improving the thickness resolution due to large numbers of comparison. To overcome this practical difficulty, an artificial neural network algorithm with several different conditions such as number of hidden layers and nodes were designed and trained within a thickness range around 100 nm. The training data set and the validation data set were used by theoretically generating an interference spectrum based on the Fresnel equation. In this work, the spectral reflectometer was realized in a wavelength range of 355 nm to 657 nm based on our previous work. For quantitative analysis in this measurement, certified reference materials having a nominal thickness of 10 nm, 30 nm, 50 nm, and 100 nm silicon dioxide thin film was measured, and an uncertainty analysis was performed on the thickness measurement value determined through this. Uncertainty factors include measurement uncertainties related to the calibration of the spectral reflectometer in use, measurement repeatability, and artificial neural network algorithms.

Conference Presentation

Citation Download Citation

Jonghan Jin and Joonyoung Lee "Thin-film thickness measurement based on spectral reflectometer using artificial neural network algorithm", Proc. SPIE 12618, Optical Measurement Systems for Industrial Inspection XIII, 1261804 (15 August 2023); https://doi.org/10.1117/12.2673342

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available