Semiconductor manufacturing management system was developed and grown up over the past decades. In order to increase the product yield and enhance the production productivity, cluster tools became the main stream in modern wafer fabrication factories which occupies over 50% of production equipment. Generally, cluster tools are integrated by several components including robots, vacuum chambers (Load locks) and single-wafer process chambers in a module and can be treated as a small factory. The throughput estimation before recipe release is very difficult. However, it is necessary and important for the planning activity. In this work, a throughput estimation model for cluster tools is proposed. The Multiple Regression Analysis is applied to develop a set of throughput estimation equations. A simulation model of cluster equipment including 3 single-wafer process chambers are built to get the historical throughput data for the regression analysis. From the Multiple Regression Analysis, it reveals that different numbers of recipes processed in the same time have to develop different regression model. The major factors in the regression model include numbers of load ports and process time of each recipe. Furthermore, a set of recipes are used to test the accuracy of estimation. Based on the testing results, they revealed that the MAPE is under 3% and the estimation model is accepted in practice to forecast the throughput of recipes for the planning activities.

Semiconductor manufacturing is a capital-intensive and high-tech industry. In order to reduce installation cost and increase production flexibility, twin-fab concept has been established over the past decade, which means two neighboring fabs can be connected to each other by automatic transportation system (AMHS). The capacity backup can be performed between twin fabs to increase whole performance. In this work, a performance evaluation model is proposed to estimate the whole production performance of twin fabs under backup policy. Two situations of capacity shortage are discussed, temporary and permanent capacity shortage. The queuing theory and Little’s Law are used in this model. Besides, the expected value is applied for the estimation of the transportation time under backup activities. Based on the evaluation model, managers can obtain an appropriate estimation of performance under capacity backup in twin-fab environment, which will help to get the reliable information for decision making.

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DRAM industry is not only among the largest manufacturing industries in the world, but also the most competitive. Furthermore, due to DRAM business is characterized by short life cycles, along with highly competition, the manufacturers are forced to migrate to advanced technology quickly. Under this circumstance, the manufacturers have to launch new technology and purchase generational equipment to meet the market demand and reduce manufacturing cost frequently. This paper investigates the technology generational transition of DRAM industry from manufacturing and planning perspectives. The concept of TOC is applied to schedule the production plan of the new/old products. Regarding to shop floor control, three definitions of cycle time are used to diagnose the production status. Finally, the workload ratio of bottleneck is used for the release decision to adjust the rhythm of production.

In this research, a model of production planning among different phases is developed. Although every phase at same giga-fab should be regarded as the similar. However, due to the different installation time, technology level and capacity allocation, the production capability of each phase will be distinguishing. Therefore, the major purpose of the production planning model is to develop a feasible and easy used algorithm to fully utilize the capacity of each phase under constraints. The concept of DBR scheduling is applied in this model to fulfill the major target. Furthermore, the notion of Technology Turn Rate (TR) is used to calculate the required date of capacity constraint resorce (CCR). Based on the prefercnce phase of products and the status of CCR, the release phase and date of each order can be well arranged.