Backpropagation (neural) networks for fast pre-evaluation of spectroscopic ellipsometric measurements

We show that a certain type of artificial neural systems or neural networks, more specifically the backpropagation network (BPN), can be an efficient tool in fast, approximate pre-evaluation of spectroscopic ellipsometric (SE) measurements. The BPN is a multilayer, feedforward network which can perform nontrivial mapping functions. We demonstrate the method on separation by implantation of oxygen (SIMOX) structure and ion implantation caused damage depth profile evaluation. The results are compared with others from independent measurements.