This AI project on smart manufacturing would like to investigate practical problems and challenges derived from our recent industrial collaboration with high-tech manufacturing companies. Although there were some manufacturing process problems that can be overcome by conventional machine learning approaches, these problems, however, have good-to-use properties or easy-to-retrieve features. As the high-tech manufacturing process “has been getting increasingly complicated, the “key” processes have become a serious challenge for most of the high-tech manufacturing companies. “We first take into consideration the lithography process in the semiconductor industry as the short-term goal to elaborate the artificial intelligence optimization applications. Apart from most advanced process control systems that used statistical measured data, we further attempt to make use of real data visualization. Next, we define the associated clusters from big data and conduct automatic compensation through deep learning approaches with artificial intelligence. We extend to some other processes under the mechanism of big data analysis as well as AI optimization applications so that the settings of these processes are no longer independent of each other. We even further exploit similar techniques in other high-tech manufacturing industries. In particular, this project receives the great support from semiconductor companies which promise to provide their data centers for our study. We also collaborate with our partner laboratories to develop compression techniques for deep neural network learning models, which can accelerate the learning and inference steps in deep learning. Moreover, based on the concept of Collective Intelligence, we study and build knowledge cases through multi-channel sources. Finally, we will establish a platform for knowledge management and decision support. The ultimate goal of this project aims to construct an Advanced Process Control and Decision Making Platform through big data mining and artificial intelligence techniques for high-tech manufacturing industries.