Abstract

Model-based infrared reflectrometry (MBIR) has been introduced recently for characterization of high-aspect-ratio deep trench structures in microelectronics. The success of this technique relies heavily on accurate modeling of trench structures and fast extraction of trench parameters. In this paper, we propose a modeling method named corrected effective medium approximation (CEMA) for accurate and fast reflectivity calculation of deep trench structures. We also develop a method combining an artificial neural network (ANN) and a Levenberg–Marquardt (LM) algorithm for robust and fast extraction of geometric parameters from the measured reflectance spectrum. The simulation and experimental work conducted on typical deep trench structures has verified the proposed methods and demonstrated that the improved MBIR metrology achieves highly accurate measurement results as well as fast computation speed.

© 2009 Optical Society of America

Fitting-determined formulation of effective medium approximation for 3D trench structures in model-based infrared reflectrometry

Chuanwei Zhang, Shiyuan Liu, Tielin Shi, and Zirong Tang
J. Opt. Soc. Am. A 28(2) 263-271 (2011)

Improved measurement accuracy in optical scatterometry using correction-based library search

Xiuguo Chen, Shiyuan Liu, Chuanwei Zhang, and Hao Jiang
Appl. Opt. 52(27) 6726-6734 (2013)

Measurement of trench depth by infrared interferometry

T. van Kessel and H. K. Wickramasinghe
Opt. Lett. 24(23) 1702-1704 (1999)

Measurement of photoresist grating profiles based on multiwavelength scatterometry and artificial neural network

Shiming Wei and Lifeng Li
Appl. Opt. 47(13) 2524-2532 (2008)

Demonstration of the feasibility of a complete ellipsometric characterization method based on an artificial neural network

Yann Battie, Stéphane Robert, Issam Gereige, Damien Jamon, and Michel Stchakovsky
Appl. Opt. 48(28) 5318-5323 (2009)