This paper surveys applications of queueing theory for semiconductor manufacturing systems (SMSs). Due to sophisticated tool specifications and process flows in semiconductor manufacturing, queueing models can be very complicated. Research efforts have been on the improvement of model assumptions and model input, mainly in the first moment (averages) and the second moment (variations). However, practices show that implementation of classical queueing theory in semiconductor industry has been unsatisfactory. In this paper, open problems on queueing modeling of SMS are discussed. A potential solution is also proposed by relaxing the independent assumptions in the classical queueing theory. Cycle time reduction has constantly been a key focus of semiconductor manufacturing. Compared with simulation, queueing theory-based analytical modeling is much faster in estimating manufacturing system performance and providing more insights for performance improvement. Therefore, queueing modeling attracts generous semiconductor research grants. Unfortunately, existing queueing models focus on simple extensions of the classical queueing theory and fail to question its applicability to the complicated SMS. Hence, related researches have not been employed widely in the semiconductor industry. In this paper, we conduct a survey on the important works and also present some open problems. We also propose a novel solution by relaxing a key assumption in the classical queueing theory. We are currently funded by Intel to explore this potential solution, and we hope it can foster an interesting research field for the years to come.

Queueing Theory for Semiconductor Manufacturing Systems: A Survey and Open Problems

Publisher Summary This chapter discusses different approaches to performance evaluation (PE) of discrete manufacturing systems that can be placed in three categories. These include static (allocation) models, aggregate dynamic models (ADMs), and detailed dynamic models. Except for the simplest of systems, ADMs usually provide estimates only of steady state performance measures (PMs), and often just average values are estimated (as opposed to say, entire distributions, or a 95% percentile). The PMs fall primarily into the category of physical PMs rather than financial PMs. The chapter also deals with physical PMs. Typical PMs estimated by an ADM include (all of these are steady state averages): utilization of each server; throughput (of production rate); flow time of a job through the system; queue length at a station; and work-in-process (WIP) in the system. These are among the primary physical PMs of interest to manufacturing personnel and, even though only steady state average values are estimated, they can be quite valuable in manufacturing decision making.

Chapter 5 Performance evaluation of production networks

To gain an edge over competitors in an increasingly global and competitive marketplace, companies today need to differentiate themselves not only in cost, but in the overall “value” of the products and the services they offer. As customers demand more and more variety of products, better, cheaper, and faster, an essential value feature for customer acquisition and retention is the ability to quote short and reliable lead times. Reliability is important for customers especially in a business-to-business setting, because it allows them to plan their own operations with more reliability and confidence [67].

Due Date Management Policies

In this paper we consider a class of job shops with a dispatch area and a machine shop, where operational controls are exercised at the dispatch area as well as at the machine shop. For such dynamic job shops with these two levels of control, there are three categories of models. They are (1) pseudo-static, (2) pure dynamic, and (3) pseudo-dynamic. In this paper approximate queueing models are developed for pure dynamic and pseudo-dynamic job shops; open queueing network models and controlled arrival single stage queueing models, respectively. The accuracy of the open queueing network models and their use are illustrated. An example indicative of the possible accuracy of the controlled arrival single stage queueing models is also given.

J.G. Shanthikumar

Papers

The time spent in a dynamic job shop

Decomposition approaches in permutation scheduling problems with application to the M-machine flow shop scheduling problems