Planning for the semiconductor manufacturer of the future

Texas Instruments (TI) is currently contracted by the Air Force Wright Laboratory and the Defense Advanced Research Projects Agency (DARPA) to develop the next generation flexible semiconductor wafer fabrication system called Microelectronics Manufacturing Science & Technology (MMST). Several revolutionary concepts are being pioneered on MMST, including the following: new single-wafer rapid thermal processes, in-situ sensors, cluster equipment, and advanced Computer Integrated Manufacturing (CIM) software. The objective of the project is to develop a manufacturing system capable of achieving an order of magnitude improvement in almost all aspects of wafer fabrication. TI was awarded the contract in Oct., 1988, and will complete development with a fabrication facility demonstration in April, 1993. An important part of MMST is development of the CIM environment responsible for coordinating all parts of the system. The CIM architecture being developed is based on a distributed object oriented framework made of several cooperating subsystems. The software subsystems include the following: process control for dynamic control of factory processes; modular processing system for controlling the processing equipment; generic equipment model which provides an interface between processing equipment and the rest of the factory; specification system which maintains factory documents and product specifications; simulator for modelling the factory for analysis purposes; scheduler for scheduling work on the factory floor; and the planner for planning and monitoring of orders within the factory. This paper first outlines the division of responsibility between the planner, scheduler, and simulator subsystems. It then describes the approach to incremental planning and the way in which uncertainty is modelled within the plan representation. Finally, current status and initial results are described

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Cycle Time Estimation in a Semiconductor Wafer Fab: A concatenated Machine Learning Approach

Die fortschreitende Digitalisierung aller Bereiche des Lebens und der Industrie lässt die Nachfrage nach Mikrochips steigen. Immer mehr Branchen – unter anderem auch die Automobilindustrie – stellen fest, dass die Lieferketten heutzutage von den Halbleiterherstellern abhängig sind, was kürzlich zur Halbleiterkrise geführt hat. Diese Situation erhöht den Bedarf an genauen Vorhersagen von Lieferzeiten von Halbleitern. Da aber deren Produktion extrem schwierig ist, sind solche Schätzungen nicht einfach zu erstellen. Gängige Ansätze sind entweder zu simpel (z.B. Mittelwert- oder rollierende Mittelwertschätzer) oder benötigen zu viel Zeit für detaillierte Szenarioanalysen (z.B. ereignisdiskrete Simulationen). Daher wird in dieser Arbeit eine neue Methodik vorgeschlagen, die genauer als Mittelwert- oder rollierende Mittelwertschätzer, aber schneller als Simulationen sein soll. Diese Methodik nutzt eine Verkettung von Modellen des maschinellen Lernens, die in der Lage sind, Wartezeiten in einer Halbleiterfabrik auf der Grundlage einer Reihe von Merkmalen vorherzusagen. In dieser Arbeit wird diese Methodik entwickelt und analysiert. Sie umfasst eine detaillierte Analyse der für jedes Modell benötigten Merkmale, eine Analyse des genauen Produktionsprozesses, den jedes Produkt durchlaufen muss – was als "Route" bezeichnet wird – und entwickelte Strategien zur Bewältigung von Unsicherheiten, wenn die Merkmalswerte in der Zukunft nicht bekannt sind. Zusätzlichwird die vorgeschlagene Methodik mit realen Betriebsdaten aus einerWafer-Fabrik der Robert Bosch GmbH evaluiert. Es kann gezeigt werden, dass die Methodik den Mittelwert- und Rollierenden Mittelwertschätzern überlegen ist, insbesondere in Situationen, in denen die Zykluszeit eines Loses signifikant vom Mittelwert abweicht. Zusätzlich kann gezeigt werden, dass die Ausführungszeit der Methode signifikant kürzer ist als die einer detaillierten Simulation

Production Scheduling

Generally speaking, scheduling is the procedure of mapping a set of tasks or jobs (studied objects) to a set of target resources efficiently. More specifically, as a part of a larger planning and scheduling process, production scheduling is essential for the proper functioning of a manufacturing enterprise. This book presents ten chapters divided into five sections. Section 1 discusses rescheduling strategies, policies, and methods for production scheduling. Section 2 presents two chapters about flow shop scheduling. Section 3 describes heuristic and metaheuristic methods for treating the scheduling problem in an efficient manner. In addition, two test cases are presented in Section 4. The first uses simulation, while the second shows a real implementation of a production scheduling system. Finally, Section 5 presents some modeling strategies for building production scheduling systems. This book will be of interest to those working in the decision-making branches of production, in various operational research areas, as well as computational methods design. People from a diverse background ranging from academia and research to those working in industry, can take advantage of this volume

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

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Metrology data modeling and data handling:capturing a domain model of ASML metrology in a software framework

During recent years, ASML metrology functionality has grown intensely. However, the software design principles (such as single responsibility, interface segregation, or open/closed principle) have sometimes been left unattended in favor of time to market. This hampers the implementation of functional design. This report describes the design and im-plementation of the metrology domain model. The domain model expresses core metrology entities, their attributes, behavior, and relationships. The Onion architecture model and domain-driven design (DDD) characterize the approach towards building the domain mod-el. Since this approach relies heavily on the dependency injection principle, the model be-comes a technology-independent core of the software implementation. The results of the project show how the metrology software can look in the future. With the isolated and ex-plicit domain model, software maintainability increases. Moreover, the domain model es-tablishes a ubiquitous language for different engineers, hence bringing the functional design closer to the software design

Designing an adaptive production control system using reinforcement learning

Modern production systems face enormous challenges due to rising customer requirements resulting in complex production systems. The operational efficiency in the competitive industry is ensured by an adequate production control system that manages all operations in order to optimize key performance indicators. Currently, control systems are mostly based on static and model-based heuristics, requiring significant human domain knowledge and, hence, do not match the dynamic environment of manufacturing companies. Data-driven reinforcement learning (RL) showed compelling results in applications such as board and computer games as well as first production applications. This paper addresses the design of RL to create an adaptive production control system by the real-world example of order dispatching in a complex job shop. As RL algorithms are “black box” approaches, they inherently prohibit a comprehensive understanding. Furthermore, the experience with advanced RL algorithms is still limited to single successful applications, which limits the transferability of results. In this paper, we examine the performance of the state, action, and reward function RL design. When analyzing the results, we identify robust RL designs. This makes RL an advantageous control system for highly dynamic and complex production systems, mainly when domain knowledge is limited

Smart Sustainable Manufacturing Systems

With the advent of disruptive digital technologies, companies are facing unprecedented challenges and opportunities. Advanced manufacturing systems are of paramount importance in making key enabling technologies and new products more competitive, affordable, and accessible, as well as for fostering their economic and social impact. The manufacturing industry also serves as an innovator for sustainability since automation coupled with advanced manufacturing technologies have helped manufacturing practices transition into the circular economy. To that end, this Special Issue of the journal Applied Sciences, devoted to the broad field of Smart Sustainable Manufacturing Systems, explores recent research into the concepts, methods, tools, and applications for smart sustainable manufacturing, in order to advance and promote the development of modern and intelligent manufacturing systems. In light of the above, this Special Issue is a collection of the latest research on relevant topics and addresses the current challenging issues associated with the introduction of smart sustainable manufacturing systems. Various topics have been addressed in this Special Issue, which focuses on the design of sustainable production systems and factories; industrial big data analytics and cyberphysical systems; intelligent maintenance approaches and technologies for increased operating life of production systems; zero-defect manufacturing strategies, tools and methods towards online production management; and connected smart factories