By Topic

Neural-net computing for interpretation of semiconductor film optical ellipsometry parameters

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Gwang-Hoon Park ; Dept. of Electr. Eng. & Appl. Phys., Case Western Reserve Univ., Cleveland, OH, USA ; Yoh-Han Pao ; B. Igelnik ; K. G. Eyink
more authors

Optical ellipsometry has been found to be a promising technique for monitoring process parameters, such as film composition and film thickness, of semiconductor wafers grown with molecular beam epitaxy. Whereas it is a straightforward task to calculate ellipsometry angles given the thickness of the film and the refractive indexes of the film and substrate, it is a difficult task to invert that mathematical relationship. However, the process must be inverted if the measured parameters are to be interpreted meaningfully in terms of film composition and film thickness. This paper reports on the use of neural-net computing for the inverse mapping of measured ellipsometry parameters. We used a functional-link net which is very efficient in function approximation. The advantage of using the net, however, is not only its speed, but also because some other net architecture characteristics allow us to perform the task in a holistic manner

Published in:

IEEE Transactions on Neural Networks  (Volume:7 ,  Issue: 4 )