INTEGRATED NEURAL NETWORK MODELING FOR ELECTRONIC MANUFACTURING

This paper addresses issues involved in the modeling of electronic manufacturing processes for optimization and control using artificial neural networks (ANNs). A modeling methodology is presented which integrates a number of techniques to counter the commonly experienced problems of selecting the ‘right’ network structure, over-training and long training times in building economical and accurate ANN models. This methodology has been implemented as an automated user-friendly ANN modeling software — CU-ANN. The main features of our methodology are data pre-processing, ‘simple to complex’ network structure approach and simultaneous training and testing. The neural networks considered have feed forward architecture and use error back-propagation algorithm for training. We have successfully applied this ANN modeling methodology to a number of simulated and real-life electronic manufacturing problems. These include stencil printing and simulated wafer fab. process data. The results indicate that our approach produces accurate, economical models and can handle a wide variety of data sets.