Artificial Neural Network (ANN)-Based Model for In Situ Prediction of Porosity of Nanostructured Porous Silicon

Abstract

Nanostructured porous silicon (PS) is usually formed by anodic etching in HF-based solution, and its porosity is measured by a destructive gravimetric technique. In this article, we report the development of an artificial neural network (ANN)-based model permitting in situ prediction of porosity of PS samples. The sensitive and nonlinear dependence of porosity on the formation parameters demanded a nonclassical treatment, and ANN was found suitable for handling this problem. A series of experiments were performed on p-type Si having resistivity 2–5 Ω-cm in 24% HF solution to generate the data for development of the ANN model. The voltage fluctuations across the electrodes during the formation of PS samples were recorded and used to develop an ANN model for prediction of voltage during the transient state of PS evolution. The predicted voltages were then used to predict porosity for different values of current density (J) at any time instant. Porosity was also measured by the conventional and destructive gravimetric method for different values of J and time. The predicted porosities agreed well with gravimetrically determined values.