Modeling and Exploratory Analysis of Discrete Event Simulation for Optimizing Overhead Hoist Transport System and Logistics in Semiconductor Manufacturing

The optimization of overhead hoist transport (OHT) systems in semiconductor manufacturing plays a crucial role in improving the production efficiency. This paper presents the development of a discrete event simulation model to analyze the physical and control characteristics of an OHT system. Additionally, evolutionary analysis is introduced to optimize the OHT operation using the developed model. The easily adjustable model design is indispensable for facilitating the analysis of OHT operation in multiple scenarios. Our modular structure is divided into three parts according to their objectives. A physical system encompasses physical entities such as equipment and vehicles. An experimental frame comprises a generator, which triggers experiments, and an analyzer of the results. Finally, a system controller is structured hierarchically and consists of an upper layer, known as the manufacturing control system, and subordinate layers. The subordinate layers are modularly divided according to their roles and encompass a main controller responsible for OHT control and a scheduling agent manager for dispatching and routing based on SEMI commands. The proposed simulation model adopts a modular structure based on discrete event system specifications for easily reconfiguring and modifying specific components. Through exploratory analysis using the simulation model, we adopt an evolutionary approach to optimize the OHT operation. The optimal operation is achieved by identifying the optimal numbers of OHT units and pieces of equipment per manufacturing zone. The experimental results of a three-scenario analysis validate the effectiveness of the proposed approach in improving key performance indicators, such as OHT utilization rates. The proposed model and analysis method seem efficient in modeling and optimizing OHT units for semiconductor manufacturing, likely enhancing production efficiency and reducing operating costs.