Evaluating the impact of process changes on cluster tool performance

Cluster tools are highly integrated machines that can perform a sequence of semiconductor manufacturing processes. Their integrated nature can complic ate-analysis when evaluating how process changes affect the overall tool pe rformance. This paper presents two integrated models for understanding the behavior of a simple, single loadlock cluster tool. The first model is a network model that evaluates the total lot processing time for a given sequence of activ ities. By including a manufacturing process model (in the form of a respons e surface model, or RSM), the model calculates the lot makespan, the total time to process a lot of wafers, as a function of the process parameter val ues and other operation times. This model allows us to quantify the sensiti vity of total lot processing time with respect to process parameters and ti mes. In addition, we present an integrated simulation model that includes a proc ess model. For a given scheduling rule that the cluster tool uses to sequen ce wafer movements, me can use the simulation to evaluate the impact of pro cess changes, including changes to product characteristics and changes to p rocess parameter values. In addition, we can construct an integrated networ k model to quantify the sensitivity of total lot processing time with respe ct to process times and process parameters in a specific scenario, We also present an evaluation of the effectiveness of two different scheduling rule s, push and pull. The examples presented here illustrate the types of insights that we can ga in from using such methods. Namely, the lot makespan is a function not simp ly of each operation's process time, but specifically of the chosen process parameter values. Modifying the process parameter values may also have sig nificant impacts on the manufacturing system performance, a consequence of importance that is not readily obvious to a process engineer when tuning a process. This result can be seen either with the decrease of raw process ti me causing little change to the makespan, or the extreme example in which t his could cause an increase in makespan because of an inefficient schedulin g rule. Additionally, because the cluster tool's maximum throughput, which is the inverse of the lot makespan, depends on the process parameters, the tradeoffs between process performance and throughput should be considered w hen evaluating potential process changes and their manufacturing impact.