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Automatic material handling system (AMHS) is becoming more important in 300 mm wafer fabrication factories (fab). Effective and efficient design and control of AMHS has become more critical particularly in capacity planning. The major concept of the AMHS capacity determination model is to maintain the originally designed optimal production throughput or cycle time of products. In order to maintain fab’s throughput or cycle time of products, WIP (work in process) portfolio of the constraint or the fastest workstation should be kept. Based on this concept, a GI/G/m queuing model based on FCFS (First-come-first-serve) dispatching rule of AMHS is applied to determine the required number of vehicles. Basically, products should be transported to the specific workstation (constraint or fastest workstation) before the workstation finishes the existing process; therefore, sufficient WIP in front of this specific workstation should be kept. Under this condition, the probability that transportation time exceeds product processing time under a certain transportation capacity level can be calculated by the proposed model. Hence, we can get the required capacity of AMHS to achieve the probability target set in advance. Due to the capacity of AMHS can be set according to the acceptable probability of non-exceeding the processing time of the constraint or fastest workstation, the level of WIP in front of this workstation can be kept. It also can be ensured that AMHS will not affect the production performance as well as keep the reasonable investment level. [ABSTRACT FROM PUBLISHER]

Time constraints related issues are unavoidable in wafer fabrications. However, when the lots released from a batch workstation overload the downstream serial workstation the consequences are more serious. Under a time constraints environment, peak workload is a critical problem that seriously hurts the overall performance of the downstream workstation. This work applies a GI/G/m queuing network to develop a capacity planning model for batch-serial processes. By applying this queuing network model, the expected waiting time between batch-serial processes can be estimated. Managers can also determine the capacity through the setting of expected rate of exceeding time constraints. The arrival smoothing of the upstream batch workstation and its effect on the downstream workstation is also analysed. The results show that arrival smoothing can effectively decrease the waiting time on the downstream serial workstation through increasing the upstream number of batch machines and decreasing the batch size. The results also conclude that increasing the number of downstream serial machines is not the only option under the batch-serial process in a time constraint environment. Therefore, an investment function of batch-serial process equipment with time constraints is established in this work that can support managers when making investment decisions. [ABSTRACT FROM AUTHOR]

A time constraint is a queue-time boundary that is set between particular sequential operations to ensure final product yield. These time boundaries, called 'sequential time constraints', can be found in a series of operations on the back-end of wafer fabrication. Wafers exceeding the time constraints are traced through the fabrication process, but generally pass through the remaining processes. Nonetheless, it is a waste of capacity to continue processing wafers with unacceptable yield. Unfortunately, these unacceptable wafers cannot be identified before the wafer acceptance test using the current control policy. This work proposes a control rule for two-level time constraints with capacity planning methodology under this rule. Wafers exceeding the lower time constraints will be treated as normal wafers; however, once wafers exceed the upper time constraint, they will be scrapped immediately. In the capacity planning model, a GI/G/m queuing network is applied to determine the required number of machines. By pre-setting target yields, the rates of wafers being marked or scrapped can be controlled. Furthermore, a novel scheme-regarding machine failures as irregular customers-is introduced to describe the effect of service interruptions. The results show that the proposed control rule and capacity planning model can more effectively resolve the issues of sequential time constraints. Moreover, the results of the analysis indicate that the current capacity expansion policy of the semiconductor industry should be re-examined. [ABSTRACT FROM AUTHOR]

Foundry companies are very concerned about fully utilizing equipment and increasing productivity because of the rapid depreciation and large investments made in equipment. Factory output is limited by its bottleneck machine. If a discrepancy exists between the planned and actual product mix, a new machine will become the bottleneck and will have a strong impact on the capacity. All famous foundry companies, such as TSMC, UMC, and others, implement backup solutions to such problems. However, the level of protective capacity at the non-bottleneck workstations strongly affects production performance. Therefore, the backup quantity should be well managed. Accordingly, a backup capacity determination model is presented in two parts: the Backup Capacity Determination Model (BCDM) and the Performance Evaluation Model (PEM). The concept of protective capacity is applied to determine the backup capacity in the BCDM. The determination of protective capacity is based on the statistical fluctuation of factors that include machine downtime and unreasonable queuing time in front of the non-bottleneck workstations. The PEM applies queuing theory and Little's Law to evaluate the effects of the backup capacity exceeding the backup capacity on throughput, work in process and the cycle time of the backup factory. A backup capacity determination model provides important information, so that managers have access to a sufficient and reliable reference when confronted with a problem that involves backup capacity. [ABSTRACT FROM AUTHOR]