Finite capacity planning algorithm for semiconductor industry considering lots priority

International audienceA finite capacity planning heuristic is developed for semiconductor manufacturing with high-mix low-volume production, complex processes, variable cycle times and reentrant flows characteristics. The proposed algorithm projects production lots trajectories (start and end dates) for the remaining process steps, estimates the expected load for all machines and balances the workload against bottleneck tools capacities. It takes into account lots' priorities, cycle time variability and equipment saturation. This algorithm helps plant management to define feasible target production plans. It is programmed in java, and tested on real data instances from STMicroelectronics Crolles300 production plant which allowed its assessment on the effectiveness and efficiency. The evaluation demonstrates that the proposed heuristic outperforms current practices for capacity planning and opens new perspectives for the production line management

Entwicklung und Einführung von Produktionssteuerungsverbesserungen für die kundenorientierte Halbleiterfertigung

Production control in a semiconductor production facility is a very complex and timeconsuming task. Different demands regarding facility performance parameters are defined by customer and facility management. These requirements are usually opponents, and an efficient strategy is not simple to define. In semiconductor manufacturing, the available production control systems often use priorities to define the importance of each production lot. The production lots are ranked according to the defined priorities. This process is called dispatching. The priority allocation is carried out by special algorithms. In literature, a huge variety of different strategies and rules is available. For the semiconductor foundry business, there is a need for a very flexible and adaptable policy taking the facility state and the defined requirements into account. At our case the production processes are characterized by a low-volume high-mix product portfolio. This portfolio causes additional stability problems and performance lags. The unstable characteristic increases the influence of reasonable production control logic. This thesis offers a very flexible and adaptable production control policy. This policy is based on a detailed facility model with real-life production data. The data is extracted from a real high-mix low-volume semiconductor facility. The dispatching strategy combines several dispatching rules. Different requirements like line balance, throughput optimization and on-time delivery targets can be taken into account. An automated detailed facility model calculates a semi-optimal combination of the different dispatching rules under a defined objective function. The objective function includes different demands from the management and the customer. The optimization is realized by a genetic heuristic for a fast and efficient finding of a close-to-optimal solution. The strategy is evaluated with real-life production data. The analysis with the detailed facility model of this fab shows an average improvement of 5% to 8% for several facility performance parameters like cycle time per mask layer. Finally the approach is realized and applied at a typical high-mix low-volume semiconductor facility. The system realization bases on a JAVA implementation. This implementation includes common state-of-the-art technologies such as web services. The system replaces the older production control solution. Besides the dispatching algorithm, the production policy includes the possibility to skip several metrology operations under defined boundary conditions. In a real-life production process, not all metrology operations are necessary for each lot. The thesis evaluates the influence of the sampling mechanism to the production process. The solution is included into the system implementation as a framework to assign different sampling rules to different metrology operations. Evaluations show greater improvements at bottleneck situations. After the productive introduction and usage of both systems, the practical results are evaluated. The staff survey offers good acceptance and response to the system. Furthermore positive effects on the performance measures are visible. The implemented system became part of the daily tools of a real semiconductor facility.Produktionssteuerung im Bereich der kundenorientierten Halbleiterfertigung ist heutzutage eine sehr komplexe und zeitintensive Aufgabe. Verschiedene Anforderungen bezüglich der Fabrikperformance werden seitens der Kunden als auch des Fabrikmanagements definiert. Diese Anforderungen stehen oftmals in Konkurrenz. Dadurch ist eine effiziente Strategie zur Kompromissfindung nicht einfach zu definieren. Heutige Halbleiterfabriken mit ihren verfügbaren Produktionssteuerungssystemen nutzen oft prioritätsbasierte Lösungen zur Definition der Wichtigkeit eines jeden Produktionsloses. Anhand dieser Prioritäten werden die Produktionslose sortiert und bearbeitet. In der Literatur existiert eine große Bandbreite verschiedener Algorithmen. Im Bereich der kundenorientierten Halbleiterfertigung wird eine sehr flexible und anpassbare Strategie benötigt, die auch den aktuellen Fabrikzustand als auch die wechselnden Kundenanforderungen berücksichtigt. Dies gilt insbesondere für den hochvariablen geringvolumigen Produktionsfall. Diese Arbeit behandelt eine flexible Strategie für den hochvariablen Produktionsfall einer solchen Produktionsstätte. Der Algorithmus basiert auf einem detaillierten Fabriksimulationsmodell mit Rückgriff auf Realdaten. Neben synthetischen Testdaten wurde der Algorithmus auch anhand einer realen Fertigungsumgebung geprüft. Verschiedene Steuerungsregeln werden hierbei sinnvoll kombiniert und gewichtet. Wechselnde Anforderungen wie Linienbalance, Durchsatz oder Liefertermintreue können adressiert und optimiert werden. Mittels einer definierten Zielfunktion erlaubt die automatische Modellgenerierung eine Optimierung anhand des aktuellen Fabrikzustandes. Die Optimierung basiert auf einen genetischen Algorithmus für eine flexible und effiziente Lösungssuche. Die Strategie wurde mit Realdaten aus der Fertigung einer typischen hochvariablen geringvolumigen Halbleiterfertigung geprüft und analysiert. Die Analyse zeigt ein Verbesserungspotential von 5% bis 8% für die bekannten Performancekriterien wie Cycletime im Vergleich zu gewöhnlichen statischen Steuerungspolitiken. Eine prototypische Implementierung realisiert diesen Ansatz zur Nutzung in der realen Fabrikumgebung. Die Implementierung basiert auf der JAVA-Programmiersprache. Aktuelle Implementierungsmethoden erlauben den flexiblen Einsatz in der Produktionsumgebung. Neben der Fabriksteuerung wurde die Möglichkeit der Reduktion von Messoperationszeit (auch bekannt unter Sampling) unter gegebenen Randbedingungen einer hochvariablen geringvolumigen Fertigung untersucht und geprüft. Oftmals ist aufgrund stabiler Prozesse in der Fertigung die Messung aller Lose an einem bestimmten Produktionsschritt nicht notwendig. Diese Arbeit untersucht den Einfluss dieses gängigen Verfahrens aus der Massenfertigung für die spezielle geringvolumige Produktionsumgebung. Die Analysen zeigen insbesondere in Ausnahmesituationen wie Anlagenausfällen und Kapazitätsengpässe einen positiven Effekt, während der Einfluss unter normalen Produktionsbedingungen aufgrund der hohen Produktvariabilität als gering angesehen werden kann. Nach produktiver Einführung in einem typischen Vertreter dieser Halbleiterfabriken zeigten sich schnell positive Effekte auf die Fabrikperformance als auch eine breite Nutzerakzeptanz. Das implementierte System wurde Bestandteil der täglichen genutzten Werkzeuglandschaft an diesem Standort

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

A method for determining tool group flexibility with uncertain machine availability - applications in a semiconductor manufacturing process / Adam Terry, Mamidala Ramulu and Posinasetti Nageswara Rao

The production of Integrated Circuits (IC) is a detailed and exacting process requiring tight specifications and precise equipment. The high cost and unique traits of this equipment requires high utilization and maximum throughput to achieve real profits. The design of fabrication facility (FAB) processes requires a thorough understanding of the adverse effects that random machine availability has on system performance. These effects (increased cycle time, decreased and variable throughput, etc) can be offset by tool group flexibility. Tool group flexibility can be described by two measures: machine flexibility (the number of tasks a machine can perform) and task flexibility (the number of machines qualified to perform a specific task). These two measures are related by the ratio of the number of machines in the tool group to the number of tasks that the group must perform. This paper utilizes a combined linear programming and simulation approach in an attempt to model the manufacturing system to gain insight into the production dynamics. The model is based on current production methodology and the use of modular equipment (steppers). The results include some insight into the added cost of flexibility and the associated production ramifications

Online Simulation in Semiconductor Manufacturing

In semiconductor manufacturing discrete event simulation systems are quite established to support multiple planning decisions. During the recent years, the productivity is increasing by using simulation methods. The motivation for this thesis is to use online simulation not only for planning decisions, but also for a wide range of operational decisions. Therefore an integrated online simulation system for short term forecasting has been developed. The production environment is a mature high mix logic wafer fab. It has been selected because of its vast potential for performance improvement. In this thesis several aspects of online simulation will be addressed: The first aspect is the implementation of an online simulation system in semiconductor manufacturing. The general problem is to achieve a high speed, a high level of detail, and a high forecast accuracy. To resolve these problems, an online simulation system has been created. The simulation model has a high level of detail. It is created automatically from underling fab data. To create such a simulation model from fab data, additional problems related to the underlying data arise. The major parts are the data access, the data integration, and the data quality. These problems have been solved by using an integrated data model with several data extraction, data transformation, and data cleaning steps. The second aspect is related to the accuracy of online simulation. The overall problem is to increase the forecast horizon, increase the level of detail of the forecast and reduce the forecast error. To provide useful forecast results, the simulation model contains a high level of modeling details and a proper initialization. The influences on the forecast quality will be analyzed. The results show that the simulation forecast accuracy achieves good quality to predict future fab performance. The last aspect is to find ways to use simulation forecast results to improve the fab performance. Numerous applications have been identified. For each application a description is available. It contains the requirements of such a forecast, the decision variables, and background information. An application example shows, where a performance problem exists and how online simulation is able to resolve it. To further enhance the real time capability of online simulation, a major part is to investigate new ways to connect the simulation model with the wafer fab. For fab driven simulation, the simulation model and the real wafer fab run concurrently. The wafer fab provides several events to update the simulation during runtime. So the model is always synchronized with the real fab. It becomes possible to start a simulation run in real time. There is no further delay for data extraction, data transformation and model creation. A prototype for a single work center has been implemented to show the feasibility

Triage and treatment of strategic and operational problems in a semiconductor equipment consumables manufacturing company

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000.Includes bibliographical references (leaf 98).The company studied in this internship project supplies consumable copper components to the semiconductor equipment industry. Growing in this industry has been much more difficult than the company's original 1995 business plan had anticipated. Customers' aversion to change, intolerance for failure, and competitive reactions (some customers are also competitors) have all taken their toll on the company's ability to establish itself in this industry. The business is currently under pressure from corporate management to improve its financial performance, which has not met corporate goals since the company's inception. It has been unable to win production volume orders from customers; thus economies of scale in production have been unachievable. Further, the delivery performance of several suppliers and the aggregate supply chain are very poor. Although the company's technology is still being developed to accommodate some unique wafer fabrication process challenges, the semiconductor industry has little tolerance for product test failures. Several such failures have degraded the company's reputation as a viable solution provider for some fab (semiconductor fabrication facility) processes. There is no systematic process for order fulfillment in place and many important tasks frequently fall between the cracks. The company is also constrained for both personnel and financial resources. A significant portion of the limited personnel resources is spent tending to emergency situations and performing repetitive tasks. Lack of personnel resources, inefficient business processes, and corporate pressures for performance improvements have necessitated a short-term business focus. Thus many strategic and fundamental issues have become secondary. This work took a triage perspective on the problems defined above. The internship began with a problem definition phase. Interviews were scheduled with each of the company's employees to define the internal perspective on the company's situation. Then, the author immersed himself in the company's value chain and took on temporary responsibility for processing several customer orders through the order fulfillment process. The author's perspective was combined with those of the internal players and a list of priority problems was created for further investigation. A common theme throughout all interviews and experiences in the value chain was a lack of understanding of costs. It quickly became clear that the organization did not have an accurate means of calculating either overhead costs or the costs of an anodizing operation across product families. In light of the recent corporate pressures to improve financial performance, this area was chosen early as one on which to focus the author's efforts. Several cost models were developed and iterated to provide insights on several cost issues. In particular, models were developed to predict the effects of demand mix and volume upon product costs, the ability of the company's anodizing facility to produce forecasted or hypothetical volume levels, the impact of reductions in anodizing line downtime downtime, and the implications for cost and relationships of a proposed contract with a new anodizing supplier. Several of these models became tools for the business to utilize in improving the accuracy of the cost estimates it uses in quoting. Other issues were much more ambiguous. In particular, the various members of the company had described the extraordinary test results achieved by their product relative to the product currently available in the market. Yet, in over three years, the company had been unable to penetrate the market with significant volumes in production fab processes. Each employee seemed to have a different opinion as to why the organization had failed to be successful. These inconsistencies drove the author to design and administer a voice-of-the-customer (VOC) survey with several different customers. The goal of the survey was to get all employees on the same page and to provide customer opinions as to what the most critical areas for improvement were. The survey uncovered several large problems. Some of the problems were too technical and lengthy in time commitment to justify further work during this project. One area that was rated as being an important problem by customers was delivery performance. In fact, this problem would become much more of a roadblock as the company got closer to winning contracts to provide consumables to production fab processes, especially given the generally high expectations of this industry and the ongoing fab efforts to reduce inventories. Delivery performance was found to be significantly affected by machining supplier delivery performance, anodizing supplier delivery performance, and internal order processing systems (or lack thereof). Each of these areas was investigated and diagnosed. Supplier discussions and surveys were critical to understanding the root cause of the supplier delivery problems.by James Ryan Griffith.S.M

Measuring machine interference to evaluate an operator cross-training program

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000.Includes bibliographical references (p. 62).by Benjamin G. Goss.S.M.M.B.A

Application of queueing theory in bulk biotech manufacturing

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, June 2011."June 2011." Page 42 blank. Cataloged from PDF version of thesis.Includes bibliographical references (p. 41).One of the most challenging problems in Amgen's biological manufacturing facility is adhering to the daily schedule of production tasks. Delays in non-time critical tasks have been traced to temporary workload surges that exceed the production staff's capability to handle them. To quantify this effect, a method for creating an M/M/c queueing model that is specific for bulk biologic manufacturing processes was developed. The model was successfully validated by comparing the predicted results to the historical data for each of the five production shifts. A discussion of how to model different improvement programs is presented, and Amgen-specific data are presented. It was found that across-the-board task duration reductions will reduce the schedule deviation rate by up to 50%. Additionally, it is shown that implementing staff-cross training with other production areas will reduce the schedule deviation rate between 14% and 75%. Implementation aspects of these improvement initiatives in a regulated production environment are discussed.by Michael Donohue.S.M.M.B.A