A Simulation Study of Automated Material Handling Systems in Semiconductor Fabs

A critical aspect of semiconductor manufacturing is the design and analysis of material handling and production control polices to optimize fab performance. As wafer sizes have increased, semiconductor fabs have moved toward the use of automated material handling systems (AMHS). However, the behavior of AMHS and the effects of AMHS on fab productivity are not well understood. The first aspect of the research involves the development of a design and analysis methodology for evaluating the throughput capacity of AMHS. A set of simulation experiments is used to evaluate the throughput capacity of an AMHS and the effects on fab performance measures. This research utilizes two simulations of SEMATECH fab data of actual production fabs. The AMHS vehicle utilization point at which fab performance is degraded is studied. Results show a large increase in lot cycle time at a vehicle utilization of 75%, far below the maximum 100% utilization. These results stress the importance of using a performance indicator that takes into account the performance of the entire fab and not only the AMHS. The second aspect of this research involves the study of AMHS and tool dispatching rules. The hypothesis of this study is that fab performance is affected by both the choice of AMHS and tool dispatching rules as well as their interaction. A full factorial design experiment is conducted to test this hypothesis. Results show that for each fab tested the vehicle rules, machine rules, and their interactions are significant using an ANOVA test on average lot cycle time and other fab performance measures. Additional analyses are conducted to identify robust combinations of AMHS and tool dispatching rules among those tested. The overall results of this study indicate that AMHS and tool dispatching rules effect fab performance and must be considered together when trying to optimize fab performance

Capacity Analysis of Automated Material Handling Systems in Semiconductor Fabs

A critical aspect of semiconductor manufacturing is the design and analysis of material handling and production control polices to optimize fab performance. As wafer sizes have increased, semiconductor fabs have moved to-ward the use of automated material handling systems (AMHS). However, the behavior of AMHS and the effects of AMHS on fab productivity is not well understood. This research involves the development of a design and analysis methodology for evaluating the throughput capacity of AMHS. A set of simulation experiments is used to evaluate the throughput capacity of an AMHS and the effects on fab performance measures. The analysis uses SEMATECH fab data for full semiconductor fabs to evaluate the AMHS throughput capacity

Development and Simulation Assessment of Semiconductor Production System Enhancements for Fast Cycle Times

Long cycle times in semiconductor manufacturing represent an increasing challenge for the industry and lead to a growing need of break-through approaches to reduce it. Small lot sizes and the conversion of batch processes to mini-batch or single-wafer processes are widely regarded as a promising means for a step-wise cycle time reduction. Our analysis with discrete-event simulation and queueing theory shows that small lot size and the replacement of batch tools with mini-batch or single wafer tools are beneficial but lot size reduction lacks persuasive effectiveness if reduced by more than half. Because the results are not completely convincing, we develop a new semiconductor tool type that further reduces cycle time by lot streaming leveraging the lot size reduction efforts. We show that this combined approach can lead to a cycle time reduction of more than 80%

Simulation in der Computer-Chip-Produktion – Möglichkeiten und Grenzen

Der Beitrag führt zuerst in die Halbleiterfertigung und die damit verbundene innerbetriebliche Logistik, dabei vor allem das Transport- und Handhabungssystem, ein. Bei der Planung und Steuerung solcher Anlagen stellen sich sehr anspruchsvolle Aufgaben, die nur mithilfe der Simulation zu lösen sind. Hierzu wird dargestellt, wie sich der Simulationseinsatz in der Halbleiterproduktion und -logistik gestaltet. Mit der Komplexität der Prozesse und Systeme wächst natürlich auch die Komplexität der eingesetzten Simulationsmodelle – auf die Frage nach einem angemessenen Abstraktionsgrad gibt es bislang jedoch keine befriedigende Antwort. Der Beitrag stellt dazu Lösungsansätze vor und zeigt, worauf künftige Forschungsarbeiten fokussieren sollten

Methodology On Investigating The Influences Of Automated Material Handling System In Automotive Assembly Process

A case study was selected as a method to collect data in actual industry situation. The study aimed to assess the influences of automated material handling system in automotive industry by proposing a new design of integration system through simulation, and analyze the significant effect and influence of the system. The method approach tool will be CAD Software (Delmia & Quest). The process of preliminary data gathering in phase 1 will collect all data related from actual industry situation. It is expected to produce a guideline and limitation in designing a new integration system later. In phase 2, an idea or concept of design will be done by using 10 principles of design consideration for manufacturing. A full factorial design will be used as design of experiment in order to analyze the performance measured of the integration system with the current system in case study. From the result of the experiment, an ANOVA analysis will be done to study the performance measured. Thus, it is expected that influences can be seen from the improvement made in the system

A product mix and a material flow problem concerning the semiconductor manufacturing industry

Das zentrale Thema dieser Arbeit behandelt die Optimierung der Programm- und Ablaufplanung in der Halbleiterindustrie. Die Diplomarbeit besteht aus zwei separaten Teilen. Der erste Abschnitt befasst sich mit einem Unternehmen aus dieser Branche namens Infineon Technologies AG. Dieser internationale Konzern dient als Beispiel für den theoretischen Hintergrund des Halbleiter-Fertigungsprozesses mit seinen spezifischen Anforderungen. Das zugrunde liegende Verfahren zeichnet sich durch große Anfälligkeit der Produkte während des Produktionsprozesses und enorme Komplexität aus. Da die gesamte Fertigung nicht als Ganzes betrachtet und optimiert werden kann, werden in der Diplomarbeit zwei unterschiedliche Problemstellungen angeführt: ein Produkt-Mix und ein Material-Flow-Problem. Dabei wird einerseits versucht, den Profit zu maximieren, andererseits soll die gesamte Herstellungszeit innerhalb einer Werkstatt minimiert werden. Diese beiden Sachverhalte werden zunächst theoretisch diskutiert und in weiterer Folge wird die mathematische Modellierung mit Xpress optimal gelöst. Das Produkt-Mix Teilproblem erfordert lediglich die Umsetzung in Xpress, da die generierten Ergebnisse Optimalität aufweisen und die Rechenzeit sich um 0 Sekunden in jedem Durchlauf bewegt. Daher wird kein weiterer Vergleich mit einer anderen Software-Implementierung dargestellt. Die generierten Lösungen des Materialfluss-Problems aus Xpress werden mit den heuristischen Ergebnissen anhand der Implementierung in C++ verglichen. Diese Ergebnisse erreichen die Optimalität nicht, sondern bieten eine gute und praktikable Lösung für eine größere Auswahl von Fällen in angemessener Rechenzeit

Reusable modelling and simulation of flexible manufacturing for next generation semiconductor manufacturing facilities

Automated material handling systems (AMHS) in 300 mm semiconductor manufacturing facilities may need to evolve faster than expected considering the high performance demands on these facilities. Reusable simulation models are needed to cope with the demands of this dynamic environment and to deliver answers to the industry much faster. One vision for intrabay AMHS is to link a small group of intrabay AMHS systems, within a full manufacturing facility, together using what is called a Merge/Diverge link. This promises better operational performance of the AMHS when compared to operating two dedicated AMHS systems, one for interbay transport and the other for intrabay handling. A generic tool for modelling and simulation of an intrabay AMHS (GTIA-M&S) is built, which utilises a library of different blocks representing the different components of any intrabay material handling system. GTIA-M&S provides a means for rapid building and analysis of an intrabay AMHS under different operating conditions. The ease of use of the tool means that inexpert users have the ability to generate good models. Models developed by the tool can be executed with the merge/diverge capability enabled or disabled to provide comparable solutions to production demands and to compare these two different configurations of intrabay AMHS using a single simulation model. Finally, results from simulation experiments on a model developed using the tool were very informative in that they include useful decision making data, which can now be used to further enhance and update the design and operational characteristics of the intrabay AMHS

Dynamic Impact for Ant Colony Optimization algorithm

This paper proposes an extension method for Ant Colony Optimization (ACO) algorithm called Dynamic Impact. Dynamic Impact is designed to solve challenging optimization problems that has nonlinear relationship between resource consumption and fitness in relation to other part of the optimized solution. This proposed method is tested against complex real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem, as well as theoretical benchmark Multi-Dimensional Knapsack problem (MKP). MMPPFO is a non-trivial optimization problem, due the nature of solution fitness value dependence on collection of wafer-lots without prioritization of any individual wafer-lot. Using Dynamic Impact on single objective optimization fitness value is improved by 33.2%. Furthermore, MKP benchmark instances of small complexity have been solved to 100% success rate where high degree of solution sparseness is observed, and large instances have showed average gap improved by 4.26 times. Algorithm implementation demonstrated superior performance across small and large datasets and sparse optimization problems.Intel Corporatio

Progress in Material Handling Research: 2012

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HEDCOS: High Efficiency Dynamic Combinatorial Optimization System using Ant Colony Optimization algorithm

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDynamic combinatorial optimization is gaining popularity among industrial practitioners due to the ever-increasing scale of their optimization problems and efforts to solve them to remain competitive. Larger optimization problems are not only more computationally intense to optimize but also have more uncertainty within problem inputs. If some aspects of the problem are subject to dynamic change, it becomes a Dynamic Optimization Problem (DOP). In this thesis, a High Efficiency Dynamic Combinatorial Optimization System is built to solve challenging DOPs with high-quality solutions. The system is created using Ant Colony Optimization (ACO) baseline algorithm with three novel developments. First, introduced an extension method for ACO algorithm called Dynamic Impact. Dynamic Impact is designed to improve convergence and solution quality by solving challenging optimization problems with a non-linear relationship between resource consumption and fitness. This proposed method is tested against the real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem and the theoretical benchmark Multidimensional Knapsack Problem (MKP). Second, a non-stochastic dataset generation method was introduced to solve the dynamic optimization research replicability problem. This method uses a static benchmark dataset as a starting point and source of entropy to generate a sequence of dynamic states. Then using this method, 1405 Dynamic Multidimensional Knapsack Problem (DMKP) benchmark datasets were generated and published using famous static MKP benchmark instances as the initial state. Third, introduced a nature-inspired discrete dynamic optimization strategy for ACO by modelling real-world ants’ symbiotic relationship with aphids. ACO with Aphids strategy is designed to solve discrete domain DOPs with event-triggered discrete dynamism. The strategy improved inter-state convergence by allowing better solution recovery after dynamic environment changes. Aphids mediate the information from previous dynamic optimization states to maximize initial results performance and minimize the impact on convergence speed. This strategy is tested for DMKP and against identical ACO implementations using Full-Restart and Pheromone-Sharing strategies, with all other variables isolated. Overall, Dynamic Impact and ACO with Aphids developments are compounding. Using Dynamic Impact on single objective optimization of MMPPFO, the fitness value was improved by 33.2% over the ACO algorithm without Dynamic Impact. MKP benchmark instances of low complexity have been solved to a 100% success rate even when a high degree of solution sparseness is observed, and large complexity instances have shown the average gap improved by 4.26 times. ACO with Aphids has also demonstrated superior performance over the Pheromone-Sharing strategy in every test on average gap reduced by 29.2% for a total compounded dynamic optimization performance improvement of 6.02 times. Also, ACO with Aphids has outperformed the Full-Restart strategy for large datasets groups, and the overall average gap is reduced by 52.5% for a total compounded dynamic optimization performance improvement of 8.99 times