Page 35 - Applied statistics and probability for engineers

Section 1-4/Probability and Probability Models 13

5"#-& t 1-2 Wire Bond Pull Strength Data

Observation Number Pull Strength y Wire Length x 1 Die Height x 2
1 9.95 2 50
2 24.45 8 110
3 31.75 11 120
4 35.00 10 550
5 25.02 8 295
6 16.86 4 200
7 14.38 2 375
8 9.60 2 52
9 24.35 9 100
10 27.50 8 300
11 17.08 4 412
12 37.00 11 400
13 41.95 12 500
14 11.66 2 360
15 21.65 4 205
16 17.89 4 400
17 69.00 20 600
18 10.30 1 585
19 34.93 10 540
20 46.59 15 250
21 44.88 15 290
22 54.12 16 510
23 56.63 17 590
24 22.13 6 100
25 21.15 5 400

conclusions for a population of objects was referred to as statistical inference. A sample of three
wafers selected from a large production lot of wafers in semiconductor manufacturing was an
example mentioned. To make good decisions, an analysis of how well a sample represents a pop-
ulation is clearly necessary. If the lot contains defective wafers, how well will the sample detect
these defective items? How can we quantify the criterion to “detect well?” Basically, how can we
quantify the risks of decisions based on samples? Furthermore, how should samples be selected
to provide good decisions—ones with acceptable risks? Probability models help quantify the
risks involved in statistical inference, that is, the risks involved in decisions made every day.

80
Pull strength 60

40
600
FIGURE 1-16 Plot 20 500
of predicted values of 0 300 400
pull strength from the 0 4 8 12 100 200 Die height
empirical model. Wire length 16 20 0