Institute for Systems Research Technical Reports

WaterSim simplifies the process of semiconductor manufacturing while teaching students engineering principles and the importance of recycling. The module was created to eliminate esoteric simulation codesand hard-to-understand models which have made it difficult to teachengineering principles to students.

As a participant in the Research Experiences for Undergraduates (REU)program, my role in the WaterSim project was a three-fold process. My firstrole was to create integrated html educational material for the simulator.

My second role was to learn about the simulation techniques that are being developed at ISR and integrate them with the Center for Environmentally-Benign Semiconductor Manufacturing (CEBSN) at the University of Arizona. This process involved creating simulation-basedlearning models with DELPHI and Vissim-based software.

My third role was to research the effects of recycling in ultra pure water (UPW)systems and its effects on a semiconductor manufacturing system. Theresults of this 8-week research include:

-- A beta version of an easy-to-use graphical user intervace called WaterSim

-- An integrated educational manual with written material

-- The formulation of web-based tutorials that exploit WaterSim's majorfeatures

-- An installation program specific to WaterSim that simplifies thedistribution of the product

-- Knowledge ofVissim and Delphi software to create simulations

With process heating lamp power held constant, wafertemperature variations of up to 160 degrees K were observed by varying feed gasH_2/N_2 ratio. Heat transfer between the wafer and susceptor wasstudied by shifting the instrumented wafer off the susceptor axis,exposing a portion of the wafer backside to the chamber floor. Modelpredictions and experimental observations both demonstrated that the gasvelocity field had little influence on the observed wafer and predictedgas temperatures.

The second model developed is an integrated simulation model that can be used when the sequence of wafer moves is not given but is determined by a scheduling rule. The model can be used to quantify the impact of changes to process parameters and product characteristics like deposition thickness on total lot processing time. The thesis contains examples that illustrate the types of insights that one can gain into cluster tool behavior from using these integrated models.

This paper presents two integrated models for understanding cluster tool behavior. The first model is a network model that evaluates the total lot processing time for a given sequence of activities. By including a manufacturing process model (in the form of a response surface model, or RSM), the model calculates the total lot processing time as a function of the process parameter values and other operation times. This model allows one to quantify the sensitivity of total lot processing time with respect to process parameters and times.

In addition, we present an integrated simulation model that includes a process model. For a given scheduling rule that the cluster tool uses to sequence wafer movements, one can use the simulation to evaluate the impact of process changes including changes to product characteristics and changes to process parameter values. In addition, one can construct an integrated network model to quantify the sensitivity of total lot processing time with respect to process times and process parameters in a specific scenario.

The examples presented here illustrate the types of insights that one can gain from using such methods. Namely, the total lot processing time is a function not simply of each operation's process time, but specifically of the chosen process parameter values. Modifying the process parameter values may have significant impacts on the manufacturing system performance, a consequence of importance which is not readily obvious to a process engineer when tuning a process (though in some cases, reducing process times may not change the total lot processing time much).

Additionally, since the cluster tool's maximum throughput depends upon the process parameters, the tradeoffs between process performance and throughput should be considered when evaluating potential process changes and their manufacturing impact.