Please use this identifier to cite or link to this item: https://doi.org/6/004
Title: A neural-network-based local-field-effect correction scheme for quantitative voltage contrast measurements in the scanning electron microscope
Authors: Chim, W.K. 
Issue Date: Jun-1996
Source: Chim, W.K. (1996-06). A neural-network-based local-field-effect correction scheme for quantitative voltage contrast measurements in the scanning electron microscope. Measurement Science and Technology 7 (6) : 882-887. ScholarBank@NUS Repository. https://doi.org/6/004
Abstract: A local-field-effect correction scheme, using a neural-network-based approach, is proposed for quantitative voltage contrast measurements (QVCM) in the scanning electron microscope (SEM). This technique showed some (though modest) improvement over an iterative correction scheme proposed previously. The correction technique also gives reasonably accurate voltage measurements on a multi-electrode test structure, even under low-extraction-field conditions for which local field effects are especially serious. The neural network employed is a back-propagation network with an adaptive learning rate to decrease the training time of the correction scheme. A momentum constant is also added to the back-propagation learning rule to minimize the chances of the network becoming stuck in a local minima of the error surface curve. The addition of momentum has a low-pass filtering effect on noise in the training data set and this could possibly account for the modest improvement in performance of this approach over the earlier iterative approach.
Source Title: Measurement Science and Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/54480
ISSN: 09570233
DOI: 6/004
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

31
checked on Dec 8, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.