Neural-net based optical ellipsometry for monitoring growth of semiconductor films

Abstract

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 indices of the film and substrate, it is a difficult task to invert that mathematical relationship. However, the process must be inverted if we wish to monitor film composition and film thickness.

This paper reports on the use of neural-nets for the inverse mapping. 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. For sufficiently accurate experimental conditions, the neural-nets may be used to monitor both film material composition and film thickness.