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

A Novel Fuzzy-Neural Slack-Diversifying Rule Based on Soft Computing Applications for Job Dispatching in a Wafer Fabrication Factory

This study proposes a slack-diversifying fuzzy-neural rule to improve job dispatching in a wafer fabrication factory. Several soft computing techniques, including fuzzy classification and artificial neural network prediction, have been applied in the proposed methodology. A highly effective fuzzy-neural approach is applied to estimate the remaining cycle time of a job. This research presents empirical evidence of the relationship between the estimation accuracy and the scheduling performance. Because dynamic maximization of the standard deviation of schedule slack has been shown to improve performance, this work applies such maximization to a slack-diversifying fuzzy-neural rule derived from a two-factor tailored nonlinear fluctuation smoothing rule for mean cycle time (2f-TNFSMCT). The effectiveness of the proposed rule was checked with a simulated case, which provided evidence of the rule’s effectiveness. The findings in this research point to several directions that can be exploited in the future

Artificial Neural Networks in Production Scheduling and Yield Prediction of Semiconductor Wafer Fabrication System

With the development of artificial intelligence, the artificial neural networks (ANN) are widely used in the control, decision‐making and prediction of complex discrete event manufacturing systems. Wafer fabrication is one of the most complicated and high competence manufacturing phases. The production scheduling and yield prediction are two critical issues in the operation of semiconductor wafer fabrication system (SWFS). This chapter proposed two fuzzy neural networks for the production rescheduling strategy decision and the die yield prediction. Firstly, a fuzzy neural network (FNN)‐based rescheduling decision model is implemented, which can rapidly choose an optimized rescheduling strategy to schedule the semiconductor wafer fabrication lines according to the current system disturbances. The experimental results demonstrate the effectiveness of proposed FNN‐based rescheduling decision mechanism approach over the alternatives (back‐propagation neural network and Multivariate regression). Secondly, a novel fuzzy neural network‐based yield prediction model is proposed to improve prediction accuracy of die yield in which the impact factors of yield and critical electrical test parameters are considered simultaneously and are taken as independent variables. The comparison experiment verifies the proposed yield prediction method improves on three traditional yield prediction methods with respect to prediction accuracy

A Fuzzy Nonlinear Programming Approach for Optimizing the Performance of a Four-Objective Fluctuation Smoothing Rule in a Wafer Fabrication Factory

In theory, a scheduling problem can be formulated as a mathematical programming problem. In practice, dispatching rules are considered to be a more practical method of scheduling. However, the combination of mathematical programming and fuzzy dispatching rule has rarely been discussed in the literature. In this study, a fuzzy nonlinear programming (FNLP) approach is proposed for optimizing the scheduling performance of a four-factor fluctuation smoothing rule in a wafer fabrication factory. The proposed methodology considers the uncertainty in the remaining cycle time of a job and optimizes a fuzzy four-factor fluctuation-smoothing rule to sequence the jobs in front of each machine. The fuzzy four-factor fluctuation-smoothing rule has five adjustable parameters, the optimization of which results in an FNLP problem. The FNLP problem can be converted into an equivalent nonlinear programming (NLP) problem to be solved. The performance of the proposed methodology has been evaluated with a series of production simulation experiments; these experiments provide sufficient evidence to support the advantages of the proposed method over some existing scheduling methods

Intelligent production control for time-constrained complex job shops

Im Zuge der zunehmenden Komplexität der Produktion wird der Wunsch nach einer intelligenten Steuerung der Abläufe in der Fertigung immer größer. Sogenannte Complex Job Shops bezeichnen dabei die komplexesten Produktionsumgebungen, die deshalb ein hohes Maß an Agilität in der Steuerung erfordern. Unter diesen Umgebungen sticht die besonders Halbleiterfertigung hervor, da sie alle Komplexitäten eines Complex Job-Shop vereint. Deshalb ist die operative Exzellenz der Schlüssel zum Erfolg in der Halbleiterindustrie. Diese Exzellenz hängt ganz entscheidend von einer intelligenten Produktionssteuerung ab. Ein Hauptproblem bei der Steuerung solcher Complex Job-Shops, in diesem Fall der Halbleiterfertigung, ist das Vorhandensein von Zeitbeschränkungen (sog. time-constraints), die die Transitionszeit von Produkten zwischen zwei, meist aufeinanderfolgenden, Prozessen begrenzen. Die Einhaltung dieser produktspezifischen Zeitvorgaben ist von größter Bedeutung, da Verstöße zum Verlust des betreffenden Produkts führen. Der Stand der Technik bei der Produktionssteuerung dieser Dispositionsentscheidungen, die auf die Einhaltung der Zeitvorgaben abzielen, basiert auf einer fehleranfälligen und für die Mitarbeiter belastenden manuellen Steuerung. In dieser Arbeit wird daher ein neuartiger, echtzeitdatenbasierter Ansatz zur intelligenten Steuerung der Produktionssteuerung für time-constrained Complex Job Shops vorgestellt. Unter Verwendung einer jederzeit aktuellen Replikation des realen Systems werden sowohl je ein uni-, multivariates Zeitreihenmodell als auch ein digitaler Zwilling genutzt, um Vorhersagen über die Verletzung dieser time-constraints zu erhalten. In einem zweiten Schritt wird auf der Grundlage der Erwartung von Zeitüberschreitungen die Produktionssteuerung abgeleitet und mit Echtzeitdaten anhand eines realen Halbleiterwerks implementiert. Der daraus resultierende Ansatz wird gemeinsam mit dem Stand der Technik validiert und zeigt signifikante Verbesserungen, da viele Verletzungen von time-constraints verhindert werden können. Zukünftig soll die intelligente Produktionssteuerung daher in weiteren Complex Job Shop-Umgebungen evaluiert und ausgerollt werden

A Distributed-Ledger, Edge-Computing Architecture for Automation and Computer Integration in Semiconductor Manufacturing

Contemporary 300mm semiconductor manufacturing systems have highly automated and digitalized cyber-physical integration. They suffer from the profound problems of integrating large, centralized legacy systems with small islands of automation. With the recent advances in disruptive technologies, semiconductor manufacturing has faced dramatic pressures to reengineer its automation and computer integrated systems. This paper proposes a Distributed- Ledger, Edge-Computing Architecture (DLECA) for automation and computer integration in semiconductor manufacturing. Based on distributed ledger and edge computing technologies, DLECA establishes a decentralized software framework where manufacturing data are stored in distributed ledgers and processed locally by executing smart contracts at the edge nodes. We adopt an important topic of automation and computer integration for semiconductor research & development (R&D) operations as the study vehicle to illustrate the operational structure and functionality, applications, and feasibility of the proposed DLECA software framewor

Space station automation study. Automation requirements derived from space manufacturing concepts. Volume 1: Executive summary

The two manufacturing concepts developed represent innovative, technologically advanced manufacturing schemes. The concepts were selected to facilitate an in depth analysis of manufacturing automation requirements in the form of process mechanization, teleoperation and robotics, and artificial intelligence. While the cost effectiveness of these facilities has not been analyzed as part of this study, both appear entirely feasible for the year 2000 timeframe. The growing demand for high quality gallium arsenide microelectronics may warrant the ventures

A Nonlinear Programming and Artificial Neural Network Approach for Optimizing the Performance of a Job Dispatching Rule in a Wafer Fabrication Factory

A nonlinear programming and artificial neural network approach is presented in this study to optimize the performance of a job dispatching rule in a wafer fabrication factory. The proposed methodology fuses two existing rules and constructs a nonlinear programming model to choose the best values of parameters in the two rules by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several studies. In addition, a more effective approach is also applied to estimate the remaining cycle time of a job, which is empirically shown to be conducive to the scheduling performance. The efficacy of the proposed methodology was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future