|Random Errors mode performs a search for random errors in layer thickness. This search is a multiparametric inverse problem which solution may be unstable and non-unique.
One of the ways to overcome the ambiguity of the reverse engineering results is to involve more input experimental data, for example, multi-scan measurements.
Random errors model assumes that different errors in coating layers. In this case the model coating is described by the vector \(X\):
\[ X=(d_1+\delta_1,…,d_m+\delta_m), \]
where \(d_1,…,d_m\) thicknesses of design layers, \(\delta_1,…,\delta_m\) absolute errors in layer thicknesses.
|Initial fitting and final fitting after application of random errors model (put the mouse on and out of the picture).|
| Random errors algorithm is base on the minimization of the discrepancy function with respect to errors in layer thicknesses:
\[ DF=DF(\delta_1,…,\delta_m)\rightarrow \min \]
In Random Errors Setup you can specify the upper boundaries of the errors. You can also specify what errors you are searching for (Active column).
|The mathematical algorithms of OptiRE help you to overcome the problems connected with the instability and non-uniqueness of reverse engineering solutions.
Reverse engineering methodology is carefully studied in our publications.
|Application of this option is demonstrated and discussed in our publications:
Look our video examples at YouTube
OptiLayer videos are available here:
Overview of Design/Analysis options of OptiLayer and overview of Characterization/Reverse Engineering options.
The videos were presented at the joint Agilent/OptiLayer webinar.