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.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.