Modeling the Contact Formation for High-Performance Silicon Solar Cells Using Polynomial Neural Networks
SungJoon Lee, Ju Hyun Park, Jae-Woong Youn, Jun Rim Choi, Seung-Soo Han · ICEIC : International Conference on Electronics, Informations and Communications · 2008
Since the neural network 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, the PNN model has been developed using the contact formation for highperformance silicon solar cells experimental data. To characterize contact formation process using PNN, co-firing was processed under varying conditions were analyzed using central composite design (CCD) with three center points. Parameters varied in these experiments included zone 1, zone 2, zone 3 temperatures and belt speed of the furnace. It was shown that the output of the PNN model follows the real experimental measurement data very well.