Aggregate modeling in semiconductor manufacturing using effective process times

In modern manufacturing, model-based performance analysis is becoming increasingly important due to growing competition and high capital investments. In this PhD project, the performance of a manufacturing system is considered in the sense of throughput (number of products produced per time unit), cycle time (time that a product spends in a manufacturing system), and the amount of work in process (amount of products in the system). The focus of this project is on semiconductor manufacturing. Models facilitate in performance improvement by providing a systematic connection between operational decisions and performance measures. Two common model types are analytical models, and discrete-event simulation models. Analytical models are fast to evaluate, though incorporation of all relevant factory-fl oor aspects is difficult. Discrete-event simulation models allow for the inclusion of almost any factory-fl oor aspect, such that a high prediction accuracy can be achieved. However, this comes at the cost of long computation times. Furthermore, data on all the modeled aspects may not be available. The number of factory-fl oor aspects that have to be modeled explicitly can be reduced signiffcantly through aggregation. In this dissertation, simple aggregate analytical or discrete-event simulation models are considered, with only a few parameters such as the mean and the coeffcient of variation of an aggregated process time distribution. The aggregate process time lumps together all the relevant aspects of the considered system, and is referred to as the Effective Process Time (EPT) in this dissertation. The EPT may be calculated from the raw process time and the outage delays, such as machine breakdown and setup. However, data on all the outages is often not available. This motivated previous research at the TU/e to develop algorithms which can determine the EPT distribution directly from arrival and departure times, without quantifying the contributing factors. Typical for semiconductor machines is that they often perform a sequence of processes in the various machine chambers, such that wafers of multiple lots are in process at the same time. This is referred to as \lot cascading". To model this cascading behavior, in previous work at the TU/e an aggregate model was developed in which the EPT depends on the amount of Work In Process (WIP). This model serves as the starting point of this dissertation. This dissertation presents the efforts to further develop EPT-based aggregate modeling for application in semiconductor manufacturing. In particular, the dissertation contributes to: dealing with the typically limited amount of available data, modeling workstations with a variable product mix, predicting cycle time distributions, and aggregate modeling of networks of workstations. First, the existing aggregate model with WIP-dependent EPTs has been extended with a curve-fitting approach to deal with the limited amount of arrivals and departures that can be collected in a realistic time period. The new method is illustrated for four operational semiconductor workstations in the Crolles2 semiconductor factory (in Crolles, France), for which the mean cycle time as a function of the throughput has been predicted. Second, a new EPT-based aggregate model that predicts the mean cycle time of a workstation as a function of the throughput, and the product mix has been developed. In semiconductor manufacturing, many workstations produce a mix of different products, and each machine in the workstation may be qualified to process a subset of these products only. The EPT model is validated on a simulation case, and on an industry case of an operational Crolles2 workstation. Third, the dissertation presents a new EPT-based aggregate model that can predict the cycle time distribution of a workstation instead of only the mean cycle time. To accurately predict a cycle time distribution, the order in which lots are processed is incorporated in the aggregate model by means of an overtaking distribution. An extensive simulation study and an industry case demonstrate that the aggregate model can accurately predict the cycle time distribution of integrated processing workstations in semiconductor manufacturing. Finally, aggregate modeling of networks of semiconductor workstations has been explored. Two modeling approaches are investigated: the entire network is modeled as a single aggregate server, and the network is modeled as an aggregate network that consists of an aggregate model for each workstation. The accuracy of the model predictions using the two approaches is investigated by means of a simulation case of a re-entrant ow line. The results of these aggregate models are promising

Predicting cycle time distributions for integrated processing workstations : an aggregate modeling approach

Predicting the cycle time distribution as a function of throughput is helpful in making a trade-off between workstation productivity and meeting due dates. To predict cycle time distributions, detailed models are almost exclusively used, which require considerable development and maintenance effort. Instead, we propose a so-called aggregate model to predict cycle time distributions, which is a lumped-parameter representation of the queueing system. The lumped parameters of the model are determined directly from arrival and departure events measured at the workstation. The paper demonstrates that the aggregate model can accurately predict the cycle time distribution of workstations in semiconductor manufacturing, in particular the tail of the distributio

Generating cycle time-throughput curves using effective process time based aggregate modeling

In semiconductor manufacturing, cycle time-throughput(CT-TH) curves are often used for planning purposes.To generate CT-TH curves, detailed simulation models or analytical queueing approximations may be used. Detailed models require much development time and computational effort. On the other hand, analytical models, such as the popular closed-form G/G/m queueing expression, may not be sufficiently accurate, inparticular for integrated processing equipment that have wafers of more than one lot in process. Recently, an aggregate simulation model representation ofworkstations with integrated processing equipment has been proposed. This aggregate model is a G=G=m type of system with a workload-dependent process time distribution, which is obtained from lot arrival and departure events. This paper presents a first proof of concept of the method in semiconductor practice. We develop the required extensions to generate CT-THcurves for workstations in a semiconductor manufacturing environment where usually only a limited amount of arrival and departure data is available. We present a simulation and an industry case to illustrate the proposed method

Order-picking workstations for automated warehouses

The FALCON (Flexible Automated Logistic CONcept) project aims at the development of a new generation of warehouses and distribution centers with a maximum degree of automation. As part of the FALCON project, this dissertation addresses the design and analysis of (automated) workstations in warehouses with an end-of-aisle order-picking system (OPS). Methods are proposed for architecting, quantifying performance, and controlling such a system. Four main topics are discussed in this dissertation. First, a modular architecture for an end-of-aisle OPS with remotely located workstations is presented. This architecture is structured into areas and operational layers. A hierarchical decentralized control structure is applied. A case of an industrial-scale distribution center is presented to demonstrate the applicability of the proposed architecture for performance analysis using the process algebra-based simulation language $\chi$ (Chi). Additionally, it is demonstrated how the architecture allows straightforward modification of the systems configurations, design parameters, and control heuristics. Second, a method to quantify the operational performance of order-picking workstations has been developed. The method is based on an aggregate modeling representation of the workstation using the EPT (Effective Process Time) concept. A workstation is considered in which a human picker is present to process one customer order at a time while products for multiple orders arrive simultaneously at the workstation. The EPT parameters are calculated from arrival and departure times of products using a sample path equation. Two model variants have been developed, namely for workstations with FCFS (First-Come-First-Serve) and for workstations with non-FCFS processing of products and orders. Both models have been validated using data from a real, operating workstation. The results show that the proposed aggregate modeling methodology gives good accuracy in predicting product and order flow time distributions. Third, the dissertation studies the design and control of an automated, remotely located order-picking workstation that is capable of processing multiple orders simultaneously. Products for multiple orders typically arrive out-of-sequence at the workstation as they are retrieved from dispersed locations in the storage area. The design problem concerns the structuring of product/order buffer lanes and the development of a mechanism that overcomes out-of-sequence arrivals of products. The control problem concerns the picking sequence at the workstation, as throughput deteriorates when a poor picking sequence is applied. An efficient control policy has been developed. Its performance is compared to a number of other picking policies including nearest-to-the-head, nearest neighbor, and dynamic programming. Subsequently, the resulting throughput and queue length distribution are evaluated under different settings. Insights for design considerations of such a system are summarized. Finally, the dissertation reflects on the findings from the proposed methods and uses them to come up with comprehensive design principles of end-of-aisle OPS with remotely located workstations. The various issues influencing the performance of such a system are highlighted. Moreover, the contribution of each proposed method with regards to these issues is delineated

The Application of Spreadsheet Model Based on Queuing Network to Optimize Capacity Utilization in Product Development

Modeling of a manufacturing system enables one to identify the effects of key design parameters on the system performance and as a result make the correct decision. This paper proposes a manufacturing system modeling approach using computer spreadsheet software, in which a static capacity planning model and stochastic queuing model are integrated. The model was used to optimize the existing system utilization in relation to product design. The model incorporates a few parameters such as utilization, cycle time, throughput, and batch size. It is predicted that design changes initiated as a result of analysis using the model reduced subsequent manufacturing costs significantly and also can reduce the launch program by a few years, because confidence in the model justified the commissioning of full-scale manufacturing equipment when the product was still only at the concept stage