Polynomial Neural Network Modeling of Reactive Ion Etching Process Using GMDH Method

Abstract

The construction of models for prediction and control of initially unknown, potentially nonlinear system is a difficult, fundamental problem in machine learning and engineering control. Since the neural network as a tool for machine learning was introduced, significant progress has been made on data handling and learning algorithms. Currently, the most popular learning algorithm in neural network training is feed forward error back-propagation (FFEBP) algorithm. Aside from the success of the FFEBP algorithm, a polynomial neural networks (PNN) learning has been proposed as a new learning method. The PNN learning is a self-organizing process designed to determine an appropriate set of Ivakhnenko polynomials that allow the activation of many neurons to achieve a desired state of activation that mimics a given set of sampled patterns. These neurons are interconnected in such a way that the knowledge is stored in Ivakhnenko coefficients. In this paper, PNN model has been developed using the nonlinear reactive ion etching (RIE) experimental data utilizing Group Method of Data Handling (GMDH). To characterize the RIE process using PNN, a low-k dielectric polymer benzocyclobutene (BCB) is etched in an SF6 and O2 plasma in parallel plate system. Data from 24 factorial experimental design to characterize etch process variation with controllable input factors consisting of the two gas flows, RF power and chamber pressure are used to build PNN models of etch rate, uniformity, selectivity and anisotropy. The modeling and prediction performance of PNN is compared with those of FFEBP. The results show that the prediction capability of the PNN models is at least 16.9% better than that of the conventional neural network models.