1. Preliminary
• Early, we may reduce the impact scope, may not predict accurately.
• There are different performance between distinct tools/products (Quality• There are different performance between distinct tools/products (Quality
of output, frequency of maintenance, PM conditions, designs…)
• Find the key factors via models (process parameters, variation of materials,
Critical dimensions…)
• Predict the variation before events and control the parameters to reduce
the variation or compensate for the imperfections to reach customers’
specifications.
Unhealthy
tool with
specific
factor
Poor quality
•What are the unhealthy
tools and the specific
factors?
•Can we control the
factors?
2015/2/5 1Yun-Hsuan Yeh
2. Data Modeling
Definition:
•Yield improve
•Cost down
Efficiency & Accuracy:
•R^2
•Number of correct
•Cost down
Observed data:
•Objects
•Indexes
•Number of correct
•Confidence interval
Review:
•Samples
•Variables
•Fitness of model
Hypotheses:
•Regression
•Classification
•Clustering
Implementation:
•Real-time insights
•Improve operations
•Innovations
2015/2/5 2Yun-Hsuan Yeh
3. Improved by Learning Process
New
samples
Model
Re-Fit with
Keep the samples
Add variable or
Change algorithm
Accuracy do not
change or
become better
Re-Fit with
new sample
Review the
samples
Change algorithm
Accuracy is worse.
Exclude the sample
effective
ineffective
2015/2/5 3Yun-Hsuan Yeh
4. Model of classification
Index 1 Index 2 Index 3 State …
1 1 4.3% 123 A …
2 1.23 3.3% 113 B …
•Separates the classes
•Prediction is made by plugging in
observed values of the attributes2 1.23 3.3% 113 B …
3 1.56 6% 156 B …
…
State B
State
State A
observed values of the attributes
into the expression.
Index 1
Index 2
Index 3
Good
Bad
Bad
Good
≥1.33
<1.33
≥120 <120
<10%
State B
≥10%
Classification tree
Bad
We can define the
health of tools
(Good/Bad) by PM
conditions, Cpk,
LRR, GRR,…
To fit the line2015/2/5 4Yun-Hsuan Yeh
5. Products / Customers
Product features /
Defects / Services
data
Fit model
Figure out the potential factors
that cause defects / customer
complains
2015/2/5 Yun-Hsuan Yeh 5
Improve imperfect design of the
product or Service method.
6. • Xs: Condition of Materials, Health of tools (Monitor items,
PM conditions, Records of maintenance, Cpk…) ,…
Correlations between process parameters,
CTQs, and Yield
PM conditions, Records of maintenance, Cpk…) ,…
• Ys: CTQs, UPH, Cost,…
• Zs: Yield, Reliability, Customer complaints ,…
Material
condition
Parameter
1
Parameter
2
Variations CTQs
Fit
Model
Fit
Model
Reliability Yield
2015/2/5 6Yun-Hsuan Yeh
7. Controlled and Uncontrolled factors
• There are many failed DOEs, since the user did not consider
the uncontrolled factors.
• It collects all potential factors (uncontrolled factors) for big
data analysis (Weather forecast).
Variation 1
Defect 1
Quality
index
Parameter2
Controlled factors Uncontrolled factors
Variation 2
Variation 3
Defect 2
Parameter 1
0.60.91.6
Parameter2
After low yield occurring, we can map out
the impact processes fast, and reduce the
time for troubleshooting or compensate for
the imperfections .
The factors (parameters) can be controlled, we
may control them to optimize the UPH or cost.
2015/2/5 7Yun-Hsuan Yeh
9. Define Target and Assumptions
We want to reduce the primary defect in FA monitor.
Skip the samples that suffered dicing shift.
Since the crack cannot be quantified, we apply the logistic
regression.
Use binary quadratic equation to fit the model
(the variables may have nonlinear relationship).
2015/2/5 9Yun-Hsuan Yeh
10. Observed data and Hypotheses
Since Length and Depth (potential factors ) have significance by
parameter estimates, we take that as variables.
Determines the impact of multiple independent variables presentedDetermines the impact of multiple independent variables presented
simultaneously predict correlation between variables and crack ratio.
Failrate
Logistic
Depth
Length
Failrate
Logistic
function
Profile statement2015/2/5 10Yun-Hsuan Yeh
11. Efficiency & Accuracy
( ) P
e
CE −
+
=
1
1
025.14019.0055.0113.0602.115.0
22
+−−+−= LDDLLDP, where
R^2=0.77, p-value<0.05, and predict with 72.6% accuracy.
Depth
e+1
Length
2015/2/5 11Yun-Hsuan Yeh
13. Review and Other Analysis
G0308 G0309
Mean 46.9 41.5
The Angles are different between Produce G0308 and G0309
to show that G0309 is better G0308.
Mean 46.9° 41.5°
STD 5° 5.54°
Crack rate 64%(60/94) 43%(13/30)
P-value=0.0001166
When the products were reworked over 2 times for LC, they
suffered serious Ni finger, but there is no crack issue. Thus,
Θ
r
suffered serious Ni finger, but there is no crack issue. Thus,
shows that Ni finger and crack is not linear relationship.
2015/2/5 13Yun-Hsuan Yeh
14. Suggestions
Project CAvailabilit
Depth
Project C
Project Risk Cost
Availabilit
y
A High Middle
New
structure
B Low High
New tool
and
accessorie
s
Length
Project A
Project B
s
C Middle Low
Tuning
recipes
2015/2/5 14Yun-Hsuan Yeh
15. Conclusion
Correlation between SMDG crack and Ni finger is nonlinear.
The model is not closed-form solution, we only provideThe model is not closed-form solution, we only provide
phenomenon and trend (do not consider all variations).
Implement project C and verify by trial run under limit
resources.
2015/2/5 15Yun-Hsuan Yeh
16. Implementation
Improve the defect ratio, yield, and reliability base on the logistic regression.
30
40
50
60
70
80
90
100
1
1.5
2
2.5
3
0
10
20
30
70um (STD) 70um + LTR 50um Deeper Notch Depth
0
0.5
1
SMDG crack %(T0) T-open%(T0) Related function fail %
2015/2/5 16Yun-Hsuan Yeh
