TABLE OF CONTENTS Six Sigma is a data-driven management system with near-perfect performance objectives (Pande et al. 2000). By data-driven we mean that in Six Sigma, the real data collected in the process under study is the only source to measure the current performance, analyze the root causes for the problem, and derive improvement strategies. Near-perfect performance objectives means that in Six Sigma, we will improve the process until it achieves a very low level of defects and a very high level of performance. Clearly, it also needs the real data from the process to verify if the desired performance requirements are met.
Data analysis is a very important part of Six Sigma. In the real business and engineering process, many data collected are random variables; that is, their value will vary with some degree of uncertainty. Let us look at Example 11.1.
In a semiconductor manufacturing process, we have a step where an oxide film is grown on a silicon wafer by using a furnace. In this step, a cassette of wafers is placed in a quartz "boat" and the boats are placed in the furnace. A gas flow is created in the furnace, and it is brought up to temperature and held there for a specified period of time. In this process, it is required that the most desirable oxide film thickness be 560 angstroms ( ); the specification of the oxide thickness is 560 100 . That is, an oxidized wafer is out of specification...
