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

Abstract

The optimization of overhead hoist transport (OHT) systems in semiconductor manufacturing plays a crucial role in improving production efficiency. In this study, the development of a discrete event simulation model to analyze the physical and control characteristics of an OHT system is presented, focusing on building a modular simulation framework for evaluating operational strategies by applying various optimization techniques. Additionally, a step-by-step analysis is introduced to optimize OHT operation using the developed model. The simulation model is broadly divided into three parts according to their purposes. The physical system encompasses the physical entities such as the equipment and vehicles. The experimental frame comprises a generator, which triggers experiments, and a result analyzer. Finally, the 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 structure based on the discrete event systems specification (DEVS). Since the hierarchical system controller may face challenges such as computational overhead and adaptability issues in real-world implementation, the modular design based on DEVS is utilized to maintain independence between layers while ensuring a flexible system configuration. Through an exploratory analysis using the simulation model, we adopt a step-by-step approach to optimize the OHT operation. The optimal operation is achieved by identifying the optimal number of OHT units and pieces of equipment per manufacturing zone. The results of the exploratory analysis for the three scenarios validate the effectiveness of the proposed framework. Increasing the number of OHT units beyond 17 resulted in only a 0.08% reduction in lead time, confirming that 17 units is the optimal number. Additionally, by adjusting the amount of equipment based on their utilization rates, we found that reducing the amount of equipment from 12 to five in process E-1 and from seven to three in the OUT process did not degrade performance. The proposed simulation framework was thus validated as being effective in evaluating OHT operational efficiency and useful for analyzing key performance indicators such as OHT utilization rates. The proposed model and analysis method effectively model and optimize OHT systems in semiconductor manufacturing, contributing to improved production efficiency and reduced operational costs. Furthermore, this work can bridge the gap between theoretical modeling and practical complexities in semiconductor logistics.