5. 5
Shainin
●
Dorian Shainin developed a series of problem
solving tools only taught by his consulting
groups
●
Multi-Vari charts
●
Full Factorials
●
B vs. C™
(using Tukey End Count)
●
Scatter Plots
●
Pre-contol
6. 6
Six Sigma
●
Attempt to control each individual process so
tight that a drift of 1.5 sigma will not create any
rejects to the agreed specification (Motorola
started, GE jumped on it).
●
...In fact, of 58 large companies that have
announced Six Sigma programs, 91 percent
have trailed the S&P 500 since, according to an
analysis by Charles Holland of consulting firm
Qualpro (which espouses a competing quality-
improvement process).
8. 8
Lean Manufacturing
●
The four goals of Lean manufacturing systems are
to:
●
* Improve quality
●
* Eliminate waste
●
* Reduce time
●
* Reduce total costs
9. 9
Poka-Yoke (Mistake proofing)
Examples of 'attention-free' Poke Yoke solutions:
●
1) a jig that prevents a part from being misoriented
during loading
●
2) non-symmetrical screw hole locations that would
prevent a plate from being screwed down incorrectly
●
3) electrical plugs that can only be inserted into the
correct outlets
●
4) notches on boards that only allow correct insertion
into edge connectors
●
5) a flip-type cover over a button that will prevent the
button from being accidentally pressed
10. 10
TRIZ, a theory of Invention
●
Altshuller screened over 1,500,000 patents looking for
inventive problems and how they were solved.
●
Only 40,000 had somewhat inventive solutions; the rest
were just improvements.
●
Altshuller more clearly defined an inventive problem as
one in which the solution causes another problem to
appear, such as increasing the strength of a metal plate
causing its weight to get heavier.
●
Usually, inventors must resort to a trade-off and
compromise between the features and thus do not
achieve an ideal solution. In his study of patents, he
found that many described a solution that eliminated or
resolved the contradiction and required no trade-off.
11. 11
TRIZ
Altshuller categorized these patents in a novel way.
●
Instead of classifying them by industry, such as
automotive, aerospace, etc., he removed the
subject matter to uncover the problem solving
process.
●
He found that often the same problems had been
solved over and over again using one of only forty
fundamental inventive principles.
●
If only later inventors had knowledge of the work
of earlier ones, solutions could have been
discovered more quickly and efficiently.
14. 14
TRIZ
●
Example, a problem in using artificial diamonds for
tool making is the existence of invisible fractures.
●
Traditional diamond cutting methods often
resulted in new fractures which did not show up
until the diamond was in use.
●
What was needed was a way to split the diamond
crystals along their natural fractures without
causing additional damage.
15. 15
TRIZ
●
A method used in food canning to split green
peppers and remove the seeds was used.
●
In this process, peppers are placed in a hermetic
chamber to which air pressure is increased to 8
atmospheres. The peppers shrink and fracture at
the stem.
●
Then the pressure is rapidly dropped causing the
peppers to burst at the weakest point and the seed
pod to be ejected.
●
A similar technique applied to diamond cutting
resulted in the crystals splitting along their natural
fracture lines with no additional damage.
18. 18
Classical
Normal curve and Ogive curve
0
5 0
1 0 0
1 5 0
2 0 0
2 5 0
3 0 0
0
5
1 0
1 5
2 0
2 5
3 0
3 0 5 0 7 0
# o f H e a d s in t r ia l
1 0 0 c o in t o s s e s , r e p e a t 2 5 0 t im e s , # o f H e a d s
b e ll
C U M
19. 19
Classical
Normal Cumulative Distribution
1
5
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
9 5
9 9
- 3
- 1 .5
0
1 .5
3
3 0 5 0 7 0
Cum%
N u m b e r o f H e a d s
R e s u lt s o f c o in f lip s
σ
C
u
m
%
Log expanding
From 50% in both directions
20. 20
1
5
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
9 5
9 9
- 3
- 1 .5
0
1 .5
3
3 0 5 0 7 0
Cum%
σ
Converting the “S” curve to a straight
line opens up many new insights
C
u
m
%
21. 21
1
5
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
9 5
9 9
- 3
- 1 .5
0
1 .5
3
3 0 5 0 7 0
Cum%
σ
Truncation of Data in Green
Fibbing going on
C
u
m
%
22. 22
Truncation of Data in Green
Fibbing going on
●
This shows screening to a specifcation tighter than
production capability. (cherry picking)
●
If the process drifts just a little, you will get no
parts.
●
This could be found at incoming QC on parts from a
supplier.
●
It also could occur in your oun process where
there is a rework for parts above or below some
limits, and operators speed up by never finding
“out of spec” parts.
●
They never “shut the process down” as they should
do in a controlled process.
23. 23
1
5
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
9 5
9 9
- 3
- 1 .5
0
1 .5
3
3 0 5 0 7 0
Cum%
σ
Variation due to two distributions with
different Std. Dev. , but the same
means mixed together
C
u
m
%
24. 24
1
5
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
9 5
9 9
- 3
- 1 .5
0
1 .5
3
3 0 5 0 7 0
Cum%
σ
Output of two different distributions
with the same std. Dev.(slope), but
different means
C
u
m
%
25. 25
Shainin
Detailed Review
●
Dorian Shainin developed a series of problem
solving tools only taught by his consulting
groups
●
Multi-Vari charts
●
Full Factorials
●
B vs. C™
(using Tukey End Count)
●
Scatter Plots
●
Pre-contol
28. 28
Pre-control
Pre-control: use of chart
1. Start process: five consecutive units in
green needed as validation of set-up.
2. If not possible: improve process.
3. In production: 2 consecutive units
4. Frequency: time interval between two
stoppages / 6.
29. 29
Evaporator #2
after crystal position change
L C F a b L i m i t e d Q u a lity P r e s e n t a tio n
E V A P O R A T O R N o 2 : N ic k e l a ft e r C r y s t a l P o s it io n c h a n g e
0 . 2 5 0
0 . 2 7 5
0 . 3 0 0
0 . 3 2 5
0 . 3 5 0
0 . 3 7 5
0 . 4 0 0
0 . 4 2 5
0 . 4 5 0
0 . 4 7 5
0 . 5 0 0
0 . 5 2 5
0 . 5 5 0
R U N N o
THICKNESSmicrons
30. 30
Shainin Clue Generation Tools
Clue-Generation Tools
Start with 20 to
1000 variables
And they are
reduced down
to 20 or fewer
Multi-Vari
Chart
Paired
Comparisons
™
Product/
Process
Search
Components
Search
™
Concentration
Chart
™
31. 31
Multi-vari Chart
●
The Multi-Vari Chart graphically shows variation of
a quality characteristic for multiple factors. The
purpose of the chart is to permit identification of
the factors having the greatest effect on variability.
●
An injection molding process produced plastic
cylindrical connectors. Two parts collected hourly
from four mold cavities for three hours consisting
of measurements at three locations on the parts.
The figure shows that cavities 2,3 and 4 had larger
diameters at the ends (top and bottom) while
cavity 1 had a taper. Thus, cavity and location
have an interacting effect.
35. 35
Tukey test procedure
• Rank individual units by parameter and
indicate Good / Bad.
• Count number of “all good” or “all bad” from
one side and vice versa from other side.
• Make sum of both counts.
• Determine confidence level to evaluate
significance.
36. 36
Tukey test confidence levels for
Tukey End Count
Total End Count Confidence
6 90%
7 95%
10 99%
13 99.9%
37. 37
Tukey test: example =7
GOOD BAD
0.007
0.011
0.014
0.015
TOP end count. All good
4
0.017
0.018
0.019
0.022
0.016
0.017
0.018
0.019
0.021
}overlap region
0.023
0.023
0.024
Bottom end count. All bad
3
40. 40
Formal DOE Tools
●
4 or fewer variables
Response
surface
Methodology
Scatter
plots
B vs. C
Variables
search
Full
Factorials
●
5 to 20 variables
1 variable Root causes distilled
Interactions presentNo interactions
Optimization
41. 41
Full-Factorial
●
A Semiconductor company was developing a
new high voltage process
●
A double base containing both Boron and
Gallium was proposed
●
The control on the gallium was so critical, that a
very expensive Ion-Implant was one of the
factors to consider, along with a novel approach
to reduce the gallium concentration with low
cost in-house chemicals
42. 42
A l u m i n u m D i f f u s i o n s , L i g h t B a s e P r o c e s s
1 0
1 0 0
1 0 0 0
1 0 0 0 0
1 2 3 4 5 6 7 8 9 1 0 1 1
A r g o n
1 2 5 0 C
D e p , 9 0 m in N 2 @
1 2 0 0
s tm -s tr ip & d r iv e
a t 1 2 5 0 N 2 O 2
R e s is tiv ity R a n g e
fo r 1 9 0 0 v - 2 2 0 0 v
N 2 1 2 5 0 C
Aluminum, light base study
43. 43
Full-Factorial
●
The questions to answer were “Can we make
the required voltage with ion implant”?
●
And “Can we find our own low cost process”?
●
The following 4 factor, 2 level DOE was run
45. 45
Check to be sure results are not
just random
1
5
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
9 5
9 9
- 3
- 1 .5
0
1 .5
3
- 5 0 0 0 - 2 5 0 0 0 2 5 0 0 5 0 0 0
Cum%
D O E m a in s + in te r a c tio n s s c o r e s
H o w t o i n t e r p r e t D O E r e s u l t s
σ
A B is f a rt h e s t
f ro m b e s t f it
46. 46
Interaction, ab
Gallium process vs. Drive gases
●
Best voltage was
A- and B+, very
costly implant and
argon
●
BUT- with the right
gases, the
combination of A+
and B- produce
acceptable voltage
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
B - ( N 2 + s t e a m ) B + ( A r g o n + H 2 )
V
o
l
t
a
g
e
A - ( I m p la n t ) A + ( in - h o u s e )
48. 48
L C F a b L i m i t e d Q u a lity P r e s e n ta tio n
All key processes are monitored
Problem areas are shaded
49. 49
Processes where a DOE resulted
in a process change are monitored
To make sure gains are realized.
Chart is marked where change
occurred and what changed.
50. 50
It looked OK at first, just as in the tests. But
then the yield dropped dramatically. Production
was stopped until the unknown issue was
resolved. That took 3 days. A quick look at
some best runs vs. worst runs showed Mesa
etch depth was the main difference. All were in
specification, but those with the deeper mesa
were better on voltage. The original tests came
through during a time the etch was running to
the deep side of the spec.
Goal was to improve 1200 volt yield DOE's were
run and a deeper base with a longer base drive
looked very good. Process was changed.
51. 51
Problems are commented on as
“Unknown”, or “Identified”-
Procedure changed on xx/xx/xxx
Chart is marked where change
occurred and what changed.
“Identified”-Mesa etch depth not
adjusted
for deeper base as needed for high
voltage program
Procedure changed on 02/17/2005
Chart is marked where change
occurred and what changed.
