Characterization of optical gratings by resolution of inverse scattering
problem has become a widely used tool. Indeed, it is known as a
non-destructive, rapid and non-invasive method in opposition with
microscopic characterizations. Use of a neural model is generally
implemented and has shown better results by comparison with other regression
methods. The neural network learns the relationship between the optical
signature and the corresponding profile shape. The performance of such a
non-linear regression method is usually estimated by the root mean square
error calculated on a data set not involved in the training process.
However, this error estimation is not very significant and tends to flatten
the error in the different areas of variable space. We introduce, in this
paper, the calculation of local error for each geometrical parameter
representing the profile shape. For this purpose a second neural network is
implemented to learn the variance of results obtained by the first one. A
comparison with the root mean square error confirms a gain of local
precision. Finally, the method is applied in the optical characterization of
a semi-conductor grating with a 1 $\mu $m period.