Page 65 - Six Sigma for electronics design and manufacturing

Six Sigma for Electronics Design and Manufacturing
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bution is an important part of calculating defects, yields, and per-
forming other statistical analyses of six sigma. In this section, the
requirements for assuming normal distribution of manufacturing
processes are examined, as well as tests that can be made to re-
view normality of data. In addition, methods for handling nonnor-
mal distribution of data for six sigma analysis are also shown.
2.1 The Quality Measurement Techniques: SQC,
Six Sigma, Cp, and Cpk
These quality techniques were developed originally for manufacturing
quality and then used for determining product design quality. Six sig-
ma has been used alternately with various assumptions of the manu-
facturing process average shift from the design specifications to set
the defect rate due to design specifications and manufacturing vari-
ability.
2.1.1 The statistical quality control (SQC) methods
Control charts have been traditionally used as the method of deter-
mining the performance of manufacturing processes over time by the
statistical characterization of a measured parameter that is depend-
ent on the process. They have been used effectively to determine if
manufacturing is in statistical control. Control exists when the occur-
rence of events (failures) follows the statistical properties of the distri-
bution of production samples.
Control charts are run charts with a centerline drawn at the man-
ufacturing process average and lines drawn at the tail of the distri-
bution at the 3 points. If the manufacturing process is under sta-
tistical control, 99.73% of all observations are within the limits of
the process. Control charts by themselves do not improve quality.
They merely indicate that the quality is in statistical “synchroniza-
tion” or “in control” with the quality level at the time when the
charts were created.
A conceptual view of control charts is given in Figure 2.1. The out-
of-control conditions indicate that the process is varying with respect
to the original period of time when the process was characterized
through the control chart, as shown in the bottom two cases. In the
bottom case, the process average is shifted to the right, whereas in the
next higher case, the process average is shifted to the left. For the two
processes shown in control, the current average of the process is equal
to the historical one that was determined when the chart was created.
The top chart shows a process that is centered with the historical av-
erage, and with a small amount of variability, indicating that the