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Schichtenmodell Verenglischen und
KommentaR
UNTEN
Interaction of Process data and Non Productive data
– a general approach
Introduction
..
Run #last
..
Run #2
Clean
Run #last
Clean
Productive
.... data
Run #1
.....
...
Measurement
Channel
Equipment perspective
(used mostly by Unit process- and
Maintenance- organizations)
Channel 1
Channel 2
Run #last
Channel 3
Channel 4
time t /s
Run #2
Run #1
..
Run #last
..
.....
...
Run #1
Run #2
Clean
....
Run #last
Clean
Productive data
including handling
Run #1
.....
...
....
Lot/Wafer perspective (used mostly by Process
integration-organizations)
 Within the Infineon APC project in the
past, the main focus was put on the
equipment perspective from which
most of the machine failures and
production time losses can be
detected.
Channel 2
Run #last
Channel 3
Channel 4
time t /s
Run #2
Run #1
..
Run #last
..
.....
...
Run #1
Run #2
Clean
....
Run #last
Clean
Run #1
.....
...
Productive data and
Non Productive data
including handling
....
Run #last
Run #2
Run #1
time t /s
Run #last
Run #2
Run #1
time t /s
M-PCA Decomposition of Trace data:
Gas flows and pressure
during handling
Channel 1 = H2_M
Channel 2 = H2_SP
Channel 3 = DOP
Channel 4 = CHPR
Tool A
Tool B
time
Analysis of equipment data using
 Time series analysis
 WER
 Outlier detection
(Limits, T2, Q)
Tool C
Lot Perspective
 The combination of the two additional
aspects (analysis of non-productive
machine data and lot perspective)
leads to the approach of machine data
analysis presented in this publication.
Run Sequence
KeyNumber x
 Also it is very important not only to
analyze, classify and supervise
machine data during production but
also in between production runs
(cleans, tests, machine checks,
conditioning).
Equipment Perspective
Data analysis
t
i
m
e
 Now the focus of FDC starts to shift
towards the lot/wafer perspective
which opens up a completely different
view on dependencies between
machine data and metrology data /
process results.
Channel 1
Recipe Step Channel n
time
Measurement
Channel
-
Run #1
....
time
 There are generally two different ways
to group the machine data for analysis,
classification and supervision.
-
.....
...
Recipe Step Channel n
 Within the last years, fault detection
and classification (FDC) based on
machine data (internal sensors) and
external sensor data became an
essential part of the production
processes.
Recipe Step Channel n
Data acquisition
Process 
Clean
Process 1
Clean 1
Process 2
...
 Metrology ... elec. Para 
Metrology
Elec. Para
…
Yield
Analysis of dependencies using:
 Correlation Analysis
 Modeling
 Decomposition
 graphical projection
Yield data, …
Application of Hotelling T2
Temperature during
Deposition processes and Cleans
Correlation Analysis
Deposition processes
and Etch back
Deposition
Productiv
Clean
1)
1)
2)
2)
Etch back
3)
3)
1)
4)
Different offset levels due to different cleaning
cycles.
1) Temperature control problems for single runs
2)
Some Runs show a different stabilization speed.
2) Different temperature behavior within several cleans
(partial correlation with 3)
3) and 4)
Variation in the shape of the curves can be
detected.
3) Temperature changes earlier - compared to the other
runs within the same lot
 Trace data was aggregated to summary data per Run.
 Correlation analysis mainly shows correlations between
summary data coming from the same process chamber
and not in between deposition and etch back (processes
independent).
 Main focus of the analysis of aggregated machine data is
to detect machine and process instabilities and not to
focus on the interaction between production processes.
Probably data acquisition and aggregation methods are
not yet adapted to show these interactions.
Conclusion
 This poster presents a different view on the machine data available besides the traditional focus of FDC.
 Machine data recorded during non productive times provides information which sometimes can not be achieved from the machine data during production.
 a FDC System should provide the possibility to gather and analyze data of the whole process including handling, cleans, tests, ...
 Machines’ non productive data can be aggregated and analyzed with the same methods like productive data.
 Nevertheless a correlation between productive data - non productive data, pre process – post process is not that easy to detect.
Dr. A. Behrisch a, J. Zimpel b
a Infineon
Technologies AG
b Advanced
Data Processing GmbH Dresden
6. European AEC/APC Conference Dublin / Ireland, April 6.-8., 2005