A novel and robust method for the accurate magnification characterization and calibration of out-of-fab SEM cluster tools

Sicignano, Al; Nikitin, A. V.; Yeremin, Dmitry; Goldburt, Tim; Tracy, Bryan

doi:10.1117/12.536047

Cited by 1 publication

(1 citation statement)

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“…As a result, there is a shift in the image points w.r.t in their projection model, which in turn decomposes into drift and distortion (Maune 1976;Mizuno et al 1997;Santo et al 2002;Sutton et al 2006;Charlot et al 2008;Vignon et al 2001;Sinram et al 2002;Cornille, 2005;Mizuno et al 1997;Sicignano et al 2004). Most of these works address the calibration of the projection and the distortion, and has very little to do with the calibration of drift.…”

Section: Related Workmentioning

confidence: 99%

Toward Fast Calibration of Global Drift in Scanning Electron Microscopes with Respect to Time and Magnification

Malti

Dembélé

Piat

et al. 2012

International Journal of Optomechatronics

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It is a well-known fact that scanning electron microscopic (SEM) image acquisition is mainly affected by nonlinearities and instabilities of the column and probe-specimen interaction; in turn, producing a shift in the image points with respect to many parameters and time, in particular. Even though this drift is comparatively less in modern SEMs, it is still an important factor to consider in most of the SEM-based applications. In this airticle, a simple and real-time method is proposed to estimate the global drift from a set of target images using image phase correlation, and to model its evolution by using the recursive equations of time and magnification. Based on the developed model, it is opted to use a Kalman filter in real time for accurate estimation and removal of the drift from the images. The developed method is tested using the images from a tungsten filament gun SEM (Jeol JSM 820) and a field effect gun SEM (FEI Quanta 200). The derived results show the effectiveness of the developed algorithm and also demonstrates its ability to be used in robotics as well as in material characterization under SEM.

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