A review of data mining applications in semiconductor manufacturing

The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.publishersversionpublishe

Analysis of production control methods for semiconductor research and development fabs using simulation

The importance of semiconductor device fabrication has been rising steadily over many years. Integrated circuit technology and innovation depends on successful research and development (R&D). R&D establishes the direction for prevailing technology in electronics and computers. To be a leader in the semiconductor industry, a company must bring technology to the market as soon as its application is deemed feasible. Using suitable production control methods for wafer fabrication in R&D fabs ensures reduction in cycle times and planned inventories, which in turn help to more quickly, transfer the new technology to the production fabs, where products are made on a commercial scale. This helps to minimize the time to market. The complex behavior of research fabs produces varying results when conventional production control methodologies are applied. Simulation modeling allows the study of the behavior of the research fab by providing statistical reports on performance measures. The goal of this research is to investigate production control methods in semiconductor R&D fabs. A representative R&D fab is modeled, where an appropriate production load is applied to the fab by using a representative product load. Simulation models are run with different levels of production volume, lot priorities, primary and secondary dispatching strategies and due date tightness as treatment combinations in a formally designed experiment. Fab performance is evaluated based on four performance measures, which include percent on time delivery, average cycle time, standard deviation of cycle time and average work-in-process. Statistical analyses are used to determine the best performing dispatching rules for given fab operating scenarios. Results indicate that the optimal combination of dispatching rules is dependent on specific fab characteristics. However, several dispatching rules are found to be robust across performance measures. A simulation study of the Semiconductor & Microsystems Fabrication Laboratory (SMFL) at the Rochester Institute of Technology (RIT) is used to verify the results

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

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

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments

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

Sustainability Benefits Analysis of CyberManufacturing Systems

Confronted with growing sustainability awareness, mounting environmental pressure, meeting modern customers’ demand and the need to develop stronger market competitiveness, the manufacturing industry is striving to address sustainability-related issues in manufacturing. A new manufacturing system called CyberManufacturing System (CMS) has a great potential in addressing sustainability issues by handling manufacturing tasks differently and better than traditional manufacturing systems. CMS is an advanced manufacturing system where physical components are fully integrated and seamlessly networked with computational processes. The recent developments in Internet of Things, Cloud Computing, Fog Computing, Service-Oriented Technologies, etc., all contribute to the development of CMS. Under the context of this new manufacturing paradigm, every manufacturing resource or capability is digitized, registered and shared with all the networked users and stakeholders directly or through the Internet. CMS infrastructure enables intelligent behaviors of manufacturing components and systems such as self-monitoring, self-awareness, self-prediction, self-optimization, self-configuration, self-scalability, self-remediating and self-reusing. Sustainability benefits of CMS are generally mentioned in the existing researches. However, the existing sustainability studies of CMS focus a narrow scope of CMS (e.g., standalone machines and specific industrial domains) or partial aspects of sustainability analysis (e.g., solely from energy consumption or material consumption perspectives), and thus no research has comprehensively addressed the sustainability analysis of CMS. The proposed research intends to address these gaps by developing a comprehensive definition, architecture, functionality study of CMS for sustainability benefits analysis. A sustainability assessment framework based on Distance-to-Target methodology is developed to comprehensively and objectively evaluate manufacturing systems’ sustainability performance. Three practical cases are captured as examples for instantiating all CMS functions and analyzing the advancements of CMS in addressing concrete sustainability issues. As a result, CMS has proven to deliver substantial sustainability benefits in terms of (i) the increment of productivity, production quality, profitability & facility utilization and (ii) the reduction in Working-In-Process (WIP) inventory level & material consumption compared with the alternative traditional manufacturing system paradigms

Intelligent shop scheduling for semiconductor manufacturing

Semiconductor market sales have expanded massively to more than 200 billion dollars annually accompanied by increased pressure on the manufacturers to provide higher quality products at lower cost to remain competitive. Scheduling of semiconductor manufacturing is one of the keys to increasing productivity, however the complexity of manufacturing high capacity semiconductor devices and the cost considerations mean that it is impossible to experiment within the facility. There is an immense need for effective decision support models, characterizing and analyzing the manufacturing process, allowing the effect of changes in the production environment to be predicted in order to increase utilization and enhance system performance. Although many simulation models have been developed within semiconductor manufacturing very little research on the simulation of the photolithography process has been reported even though semiconductor manufacturers have recognized that the scheduling of photolithography is one of the most important and challenging tasks due to complex nature of the process. Traditional scheduling techniques and existing approaches show some benefits for solving small and medium sized, straightforward scheduling problems. However, they have had limited success in solving complex scheduling problems with stochastic elements in an economic timeframe. This thesis presents a new methodology combining advanced solution approaches such as simulation, artificial intelligence, system modeling and Taguchi methods, to schedule a photolithography toolset. A new structured approach was developed to effectively support building the simulation models. A single tool and complete toolset model were developed using this approach and shown to have less than 4% deviation from actual production values. The use of an intelligent scheduling agent for the toolset model shows an average of 15% improvement in simulated throughput time and is currently in use for scheduling the photolithography toolset in a manufacturing plant