A combined topographical search strategy with ellipsometric application

A comparison deals with the advantages and disadvantages of the classical r andom-base, exhaustive and gradient searches and presents a precise local s earch combined global search control strategy including a new, systematic p oint selection which makes possible the escape from local minima by time. A s a demonstration electrochemically etched porous silicon (PS) samples were investigated by spectroscopic ellipsometry (SE). The evaluation process (a global optimisation task) was made in different ways to see the difficulti es and the differences among the evaluating possibilities. The new, topogra phical search (named Gradient Cube search) was compared with some classical methods (Grid search, Random or Monte-Carlo search, and Levenberg-Marquard t gradient search) and with two more complex algorithms (Genetic Algorithms and Simulated Annealing) by evaluating real measurements. The application results prove that the classical methods have difficulties to give enough r eliability and precision at the same time in global optimisation tasks if t he error surface is hilly. There is therefore a hard need of escaping from local minima, and a need of a systematic evaluation to avoid the uncertaint y of random-base evaluation. The Gradient Cube search is an effective, syst ematic hill-climbing search with high precision and so it can be useful in ellipsometry.