Artificial Neural Network Modeling Of Czochralski Single Crystal Growth

Abstract

The adaptive nature of Artificial Neural Networks (ANNs) makes them a powerful tool for modeling complex processes. ANNs are inherently nonlinear and process signals in a parallel manner. These qualities make them suitable for applications requiring the model to identify the dynamics of a nonlinear system and applications in which the speed of the model's response is important. Czochralski single crystal growth is a widely used technique for growing semiconductor crystals to be used for substrate material in integrated circuits. The trend in the microelectronics industry is to fabricate smaller components and more intricate circuits while increasing the wafer size on which these. circuits are made. This trend requires that larger crystals be grown. In addition, the purity and defect requirements are becoming more stringent. Czochralski crystal growth is a nonlinear process and difficult to control. The trends in industry make the growth even more sensitive to the control. An ANN is developed in this research that could be used for a model based control of the Czochralski crystal growth process. The ANN model is compared with an existing model developed for germanium single crystal growth.