Paper
2 February 2009 Metrology of deep trench structures in DRAM using FTIR reflectance spectrum
Chuanwei Zhang, Shiyuan Liu, Tielin Shi
Author Affiliations +
Abstract
The demand for advanced DRAM technologies with smaller ground rules lead to new challenges for online metrology of high aspect-ratio deep trench structure. In addition to common metrology methods like ellipsometry, scanning electron microscopy (SEM), and atomic force microscopy (AFM), the use of further measurement techniques are needed for advanced node technology. This paper proposes a technique for metrology of critical dimension (CD) of deep trenches formed as capacitors in advanced dynamic random access memory (DRAM) using Fourier-transform infrared (FTIR) reflectance spectrometry. This technique is based on a fast but accurate modeling of the complex periodic deep trench structure, which is represented as a multilayer thin film stack with a combination of homogeneous layers on the silicon substrate using effective medium approximation approach. The metrology problem is solved by an artificial neural network (ANN) and Levenberg-Marquardt (LM) combined algorithm to find the values of the modeling parameters which should produce a best fit to the given measured spectrum. This technique is validated by simulation of extracting geometry parameter of actual DRAM trench structures, and demonstrated to be an adequate approach for the determination of the structure parameters.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chuanwei Zhang, Shiyuan Liu, and Tielin Shi "Metrology of deep trench structures in DRAM using FTIR reflectance spectrum", Proc. SPIE 7160, 2008 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Applications, 716017 (2 February 2009); https://doi.org/10.1117/12.808080
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Cited by 1 scholarly publication.
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KEYWORDS
Reflectivity

Metrology

FT-IR spectroscopy

Infrared radiation

Infrared spectroscopy

Silicon

Artificial neural networks

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