Paper
21 May 1996 Collection of low-energy secondary electrons and imaging in a low-voltage SEM
Diana Nyyssonen
Author Affiliations +
Abstract
One of the factors that influences the nature of secondary electron imaging and the subsequent metrology of submicron features in a SEM is the system's ability to collect low energy secondary electrons (LES). In a SEM, a bias voltage is applied to the detector grid to enhance collection and pull electrons from the wafer. However, at low beam voltages the bias voltage that can be applied is limited in order not to deflect or interfere with the incident beam. For even moderate aspect ratios of submicron features (3:1), there is a reduction in the collection efficiency that may affect imaging. In this paper, we use a simple model of LES trajectories to simulate both symmetric and asymmetric collection systems. Formulas for the collection angles are given as a function of the aspect ratio of the feature and parameters of the collection system. These formulas can be incorporated into Monte Carlo, diffusion or other phenomenological modeling to predict the effect of collection system design on SEM imaging. An example is given using a previously published model [Nyyssonen, Proc. SPIE Vol. 921, pp. 48 - 56, 1988]. This model computes the surface integral of a probability density function to predict SEM images. It is shown that reduced LES collection efficiency may introduce artifacts in the SEM image of both lines and trenches. These artifacts can be used as a method of characterizing the efficiency of the collection system when well- characterized samples are used.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Diana Nyyssonen "Collection of low-energy secondary electrons and imaging in a low-voltage SEM", Proc. SPIE 2725, Metrology, Inspection, and Process Control for Microlithography X, (21 May 1996); https://doi.org/10.1117/12.240145
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Cited by 1 scholarly publication.
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KEYWORDS
Scanning electron microscopy

Electrons

Imaging systems

Monte Carlo methods

Systems modeling

Sensors

Silicon

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