Justification for using DES modelling

Simulation modelling of semiconductor manufacturing plants is a topic of great interest and is much commented on in the associated literature. Banks and Norman (1995) stated

that “many companies are discovering that the value of simulation software goes beyond its ability to offer a peek into the future. It has numerous other benefits, including its ability to help managers make better decisions, explore possibilities, understand why certain phenomena occur, identify constraints and diagnose problems”. The feeling in the industry according to Atherton and Atherton (1995); Miller (1990); Rubinstein and Melamed (1998) is that simulation is the best approach for modelling semiconductor fabs. This is also true for generating operating curves, Fowler et al. (2001) stated that “as the system increases in complexity, simulation analysis becomes the most viable approach for generating the curve”.

Carson (2005) suggested several situations when simulation is most useful. These sce- narios, are listed below and a case is made for each point, regarding the use of simulation to generate operating curves in this thesis.

1. There is no simple analytical solution available or such a solution does not offer the required accuracy. The reasons for not using analytical models when attempting to generate operating curves for semiconductor fabs is outlined in detail in Section 2.4. 2. The real system under investigation can be captured, i.e., it is possible to build a logical interpretation (conceptual model) of the system that describes the real system to a required degree of accuracy. This is possible, but a non-trivial task, as was shown by Boning et al. (1992) and Sprenger and Rose (2010), who enabled their models by capturing the dominant structures and phenomena of a semiconductor manufacturing system and conceptualised them into modelling components. The development chapters in this thesis discuss the building of a conceptual modelling framework for semiconductor wafer fabs.

3. If the system is new or not yet built, or requires major configuration changes that will have a significant impact on the system. The assumption here is that the overall system remains relatively fixed but can change within the boundaries of the specification, e.g., the addition or removal of a tool from a toolset or changing the number of maintenance technicians.

4. The changes to the real system being considered require significant investment and demand a high probability of success. The purpose of this thesis is proof of concept rather than an actual investment analysis.

5. Some forum is in place (or can be created) where the simulation team and all other parties including management, clients and people in the real system being modelled, can communicate easily and discuss and agree on the assumptions documents. The stakeholders in the models built to generate operating curves partook in regular meetings to discuss the project, though no official forum was put in place. It is worth noting however, that such a collaborative environment may be conducive to system learnings outside of the scope of the modelling project. This was shown by Potti and Whitaker (2003), who used their model as the focal point for all communication between fab departments regarding productivity improvement projects.

6. There is some type of animation available. Animation increases the chances of a more credible simulation model that is understood and trusted by those who have invested in it and also the end-users. Implementations of the DES models in this thesis were created using ExtendSim, a graphical simulation modelling tool capable of both 2-D and 3-D animation as well as basic ‘proof animation’. Some of the models were built in SimPy, a library for Python. These models do not support animation but Python has a number of libraries including Pyglet and Pygame that could be used to animate the SimPy simulation models.

Many of these recommendations were also discussed by Banks and Gibson (1996, 1997a) and listed below. Again, a justification for using simulation modelling in the context of this thesis is offered in Table 2.1.

Banks and Gibson further stressed that simulations do not provide an optimal solu- tion, that is, they cannot recommend a system configuration that the analyst does not specifically investigate. This is a valid statement but does not interfere with the aims of the methodology in this thesis, which uses graphical comparisons between resultant operating curves to analyse various configurations. The assumption is that the user of the modelling applications will be knowledgeable about the system, and can offer alternate configurations, test them, and assess them based on their impact on the operating curve output from the model.

When using simulation, the advantages tend to lie in the areas of general applicability and capability. Typically, most complexities can be modelled, the only limiting factor is the cost and time-frame of the project. Another benefit to using simulation is that it is possible to model transient behaviour. Klein and Kalir (2006) discussed this type

Table 2.1: Justification for using simulation modelling to generate operating curves in semiconductor manufacturing, based on the recommenda- tions offered by Banks and Gibson (1996, 1997a).

Circumstances Justification

A common sense analysis is available. Capturing semiconductor manufacturing systems in detail is a non-trivial task and it is highly unlikely that a common sense analysis is available that can capture the complexities sufficiently to generate an operating curve.

An analytical solution is more appro- priate.

Section 2.4 details the reasons why an analytical modelling approach to generating operating curve for semiconductor fabs is alone insufficient.

Direct experimentation with the real system is easier.

This could be very costly in a fab, and have a nega- tive impact on production targets.

The simulation costs exceed the re- wards.

The models generated in this thesis are proof of con- cept. However, the modelling strategy implemented was designed to minimise the labour involved in gen- erating an operating curve via simulation models. Hence a relatively low cost would be required to gen- erate a curve which may be highly valuable to the fab.

Simulation resources and expertise is not available.

The models and applications developed in this thesis are tailored to use by non-simulationists.

There is insufficient time to perform the simulation analysis.

The models and applications arising from this the- sis are fully automated requiring very little analysis time.

There is no data available. It is assumed that the programs and applications have access to factory data which is electronically stored. This is generally a reasonable assumption in highly automated semiconductor manufacturing sys- tems where an abundance of data is recorded. The model can’t be verified or vali-

dated.

A full validation and verification of all models is per- formed.

The systems behaviour is too complex to be captured.

The highly complex nature of semiconductor manu- facturing will be modelled by modularising its most common aspects into repeatable and reusable DES models.

of modelling and the benefits of using it to monitor a fab undergoing ramping-up to a new product. For these reasons simulation is currently leading the line for modelling semiconductor manufacturing.

In fact, some simulation models may become such an important tool for factory man- agement that it can drive most, if not all of the decisions made. An example of this was shown by Potti and Whitaker (2003), who used their simulation model as the focal point for all communication between fab departments regarding productivity improve- ment projects.