Equipment Utilization Enhancement in Photolithography Area Through a Dynamic System Control Using Multi-Fidelity Simulation Optimization With Big Data Technique

Abstract

Photolithographic (Photo) plays a key role in semiconductor manufacturing because of its importance to advanced process shrinking. Even with a small improvement in its operational efficiency, the cost competitiveness in production can be enhanced as a result of the huge amount of share capital cost. However, it is difficult to stabilize the throughput rhythm of Fabs, while keeping a high equipment utilization for Photo. In the light of Industry 4.0 and big data, a huge potential of maintaining a desired system performance by (near) real-time dynamic system control is highly anticipated. But it also poses challenges to intelligently handling mass data acquisition and allocating computing resources. This research aims to maximize the equipment utilization in Photo by an efficient multi-model simulation optimization approach with big data techniques in the era of Industry 4.0. dynamic Photo configurator and abnormality detector are the two critical units in our proposed system framework; the former can make a quick decision to optimize the system configuration while receiving the adjustment request from the latter. The results from an empirical study show the practical viability of proposed approach that the capacity loss in Photo has been effectively improved.