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Microsystems packaging is fundamentally dependent on the manufacture of microelectronic, photonic, radio frequency (RF), and MEMS devices. The system-on-package (SOP) approach has been identified as a key strategy for integrating these strategic packaging technologies. Because of rising costs, the challenge before SOP manufacturers is to offset capital investment with greater automation and technological innovation in the fabrication process. To reduce manufacturing cost, several important subtasks have emerged, including increasing fabrication yield, reducing product cycle time, maintaining consistent levels of product quality and performance, and improving the reliability of processing equipment. Because of the large number of steps involved, maintaining product quality in an SOP manufacturing facility requires the control of hundreds of process variables. The interdependent issues of high yield, high quality, and low cycle time are addressed by the ongoing development of several critical capabilities in state-of-the-art computer-integrated manufacturing (CIM) systems: in situ process monitoring, process/equipment modeling, real-time process control, and equipment diagnosis. Recently, the use of computational intelligence in various manufacturing applications has increased, and the SOP manufacturing arena is no exception to this trend. Artificial neural networks, genetic algorithms (GAs), and other techniques have emerged as powerful tools for assisting CIM systems in performing various process monitoring, modeling, and control functions. This paper reviews current research in these areas, as well as the potential for deployment of these capabilities in state-of-the-art SOP manufacturing facilities.