Virtual Metrology to Measure Mass Loss at Deep Trench ProcessesApril 18th, 2007
Tilo Wünsche, Matthias Rudolph, Jan Zimpel (adp GmbH)
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 2 Copyright © Qimonda AG 2007 · All rights reserved.
Motivation to Measure the Mass Loss during the Deep Trench Etch Process
• Deep trench used as storage capacitor
• Capacitance is one of main contributors to functionality
• Capacitance depends on area of capacitor plate (trench sidewall)
• Si mass loss is indicator for sidewall area
• Deep trench used as storage capacitor
• Capacitance is one of main contributors to functionality
• Capacitance depends on area of capacitor plate (trench sidewall)
• Si mass loss is indicator for sidewall area
mSiSi
hardmask
Si
hardmask
DT etch
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 3 Copyright © Qimonda AG 2007 · All rights reserved.
DTmassLoss
Ca
pa
cit
y
mass loss
Cap
acity
of D
RA
M
Motivation to Measure the Mass Loss during the Deep Trench Etch Process
Virtual Metrology predicting Mass loss
• Value for each wafer (high sample rate)
• Little cycle time consumption
Virtual Metrology predicting Mass loss
• Value for each wafer (high sample rate)
• Little cycle time consumption
Mass loss
• Correlates with Capacity of storage capacitor
• Parameter for short loop control
Measurement of weight before and after etch necessary
not all wafers can be measured, because of time consumption
Mass loss
• Correlates with Capacity of storage capacitor
• Parameter for short loop control
Measurement of weight before and after etch necessary
not all wafers can be measured, because of time consumption
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 4 Copyright © Qimonda AG 2007 · All rights reserved.
Offline AnalysisOffline Analysis
Data Mining via Ridge Regression / define Areas
Etch Process / Recording spectraEtch Process / Recording spectra
Outline of the PresentationScheme of Data Processing
On-line Application of Model On-line Application of Model
Applying Model to APC Trend
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
Preprocessing of OES Data300 400 500 600 700
-1000
-500
0
500
1000
1500
Plasma
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 5 Copyright © Qimonda AG 2007 · All rights reserved.
Offline AnalysisOffline Analysis
Data Mining via Ridge Regression / define Areas
Outline of the PresentationScheme of Data Processing
On-line Application of Model On-line Application of Model
Applying Model to APC Trend
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
Preprocessing of OES Data300 400 500 600 700
-1000
-500
0
500
1000
1500Etch Process / Recording spectraEtch Process / Recording spectra
Plasma
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 6 Copyright © Qimonda AG 2007 · All rights reserved.
Measure and Processing OES DataSensor Integration
OES Analysis ApplicationOES Analysis ApplicationOES Measurement ApplicationOES Measurement Application
• Spectral data visualization
• Data mining (PCA, Modeling)
• EP model design
• On-line process monitoring
Equipment HOST
FAB LAN
Plasma
optical fiber
OES Sensor
SpectralDatabas
e
recipe start with logistic (MID, Slot, Wafer, Recipe) recipe stop, recipe step
OES Data
MID: XYZSLOT: 1Recipe: ABC
FDC ApplicationFDC Application
• Visualization of process indicators
• OCAP
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 7 Copyright © Qimonda AG 2007 · All rights reserved.
Information of the OES Spectrum
Response from NF3
Response from NF3
Response from SiF4
Response from SiF4
Response from HBrResponse from HBr
Break ThroughMain Etch
Plasma interactions too many gas species and other process parameters
• Huge amount of optical emission lines
• Complex dependency of emission strength for individual species
Spectral responses characterized on experimental variations of HBr, NF3, Ar, O, SiF4
Plasma interactions too many gas species and other process parameters
• Huge amount of optical emission lines
• Complex dependency of emission strength for individual species
Spectral responses characterized on experimental variations of HBr, NF3, Ar, O, SiF4
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 8 Copyright © Qimonda AG 2007 · All rights reserved.
Offline AnalysisOffline Analysis
Outline of the PresentationScheme of Data Processing
On-line Application of Model On-line Application of Model
Applying Model to APC Trend
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
Preprocessing of OES Data
Etch Process / Recording spectraEtch Process / Recording spectra
Plasma
Data Mining via Ridge Regression / define Areas
300 400 500 600 700-1000
-500
0
500
1000
1500
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 9 Copyright © Qimonda AG 2007 · All rights reserved.
Data Mining – Task
mas
s lo
ss
Objective: Extraction of significant spectral information representing mass loss during etch process
Solution: Decomposition of the data cube by unfolding and ridge regression or PCA based methods
Objective: Extraction of significant spectral information representing mass loss during etch process
Solution: Decomposition of the data cube by unfolding and ridge regression or PCA based methods
wavelength wafer 1 ... N
etch
tim
e t
High dimensional data cube of OES spectra containing information
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 10 Copyright © Qimonda AG 2007 · All rights reserved.
Data Mining – Ridge Regression – I
Ridge Regression:
• Method to solve an overdetermined system of equations
• Favorable with many collinear data sets, e.g. spectral data
creates a model using all predictors
3-way ridge regression Model allow the extraction of significant spectral ranges and information about important time ranges which carry information about mass loss during etch process
Ridge Regression:
• Method to solve an overdetermined system of equations
• Favorable with many collinear data sets, e.g. spectral data
creates a model using all predictors
3-way ridge regression Model allow the extraction of significant spectral ranges and information about important time ranges which carry information about mass loss during etch process
200 300 400 500 600 700 800
400
450
500
550
600
650
-2
-1.5
-1
-0.5
0
0.5
1
1.5
time /s
wav
elen
gth
(nm
)
426 /440nm
657 nm
548 nm
519 nm
Step1 Step2 Step3
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 11 Copyright © Qimonda AG 2007 · All rights reserved.
Data Mining – Ridge Regression – II
Whole response pattern could be used as a model
Whole response pattern could be used as a model
To predict the mass loss model some significant spectral ranges are sufficient.
Simple automated updated procedure possible
Robust model
To predict the mass loss model some significant spectral ranges are sufficient.
Simple automated updated procedure possible
Robust model
200 300 400 500 600 700 800
400
450
500
550
600
650
-2
-1.5
-1
-0.5
0
0.5
1
1.5
115 120 125 130 135 140 145 150120
125
130
135
140
145
150
155 R=0.80819
kx=1
1/ky=1.531
k0=1.2373
mas
s lo
ss
predicted mass loss
time (s)
wav
elen
gth
(nm
)
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 12 Copyright © Qimonda AG 2007 · All rights reserved.
Offline AnalysisOffline Analysis
Outline of the PresentationScheme of Data Processing
On-line Application of Model On-line Application of Model
Applying Model to APC Trend
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
Etch Process / Recording spectraEtch Process / Recording spectra
Plasma
Data Mining via Ridge Regression / define Areas
300 400 500 600 700-1000
-500
0
500
1000
1500
Preprocessing of OES Data
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 13 Copyright © Qimonda AG 2007 · All rights reserved.
Long Term Process Effects
Chamber pollutes during production
Chamber has to be cleaned, worn parts have to be changed
Production recipe has to be adapted to meet changing conditions
Chamber pollutes during production
Chamber has to be cleaned, worn parts have to be changed
Production recipe has to be adapted to meet changing conditions
runs
mas
s lo
ss
Process deviationProcess deviationMaintenance activitiesMaintenance activities
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 14 Copyright © Qimonda AG 2007 · All rights reserved.
Preprocessing of OES Data
Normalization to step over wet cleancycles
Best results by normalization on baseof total intensity of the actual measured spectrum (e.g. integral or norm)
Normalization to step over wet cleancycles
Best results by normalization on baseof total intensity of the actual measured spectrum (e.g. integral or norm)
8000
9000
10000
11000
12000
13000
14000
15000
16000
20 40 60 80 100 120 140 160-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
HBr Response
Data filtering to exclude
• Measurement failures
• Bad processes
Only real outliers should be removed
Distribution function of predictors and response have to be kept
Data filtering to exclude
• Measurement failures
• Bad processes
Only real outliers should be removed
Distribution function of predictors and response have to be kept
Increase of intensity after wet cleanIncrease of intensity after wet clean
OutliersOutliers
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 15 Copyright © Qimonda AG 2007 · All rights reserved.
Offline AnalysisOffline Analysis
Outline of the PresentationScheme of Data Processing
On-line Application of Model On-line Application of Model
Applying Model to APC Trend
Etch Process / Recording spectraEtch Process / Recording spectra
Plasma
Data Mining via Ridge Regression / define Areas
300 400 500 600 700-1000
-500
0
500
1000
1500
Preprocessing of OES Data
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 16 Copyright © Qimonda AG 2007 · All rights reserved.
Search for best correlating predictorSearch for best correlating predictor
Ypre = A*x + B*x
Add predictor to the model
Ypre = A*x + B*x
Add predictor to the model
Forward Regression
Forward Regression:
• Method to solve an overdetermined system of equations
• Favorable with collinear data sets
• Selects to most correlation predictors and skips the others
Creates simple models (little calculation power, easily to implement as equation)
Forward Regression:
• Method to solve an overdetermined system of equations
• Favorable with collinear data sets
• Selects to most correlation predictors and skips the others
Creates simple models (little calculation power, easily to implement as equation)
NoFinishedFinished
Yesp-value
Check if correlation has highly probability
p-value
Check if correlation has highly probability
Err = Ypre - Yact
Apply model and calculate residual error
Err = Ypre - Yact
Apply model and calculate residual error
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 17 Copyright © Qimonda AG 2007 · All rights reserved.
Static Modelm
ass
loss
predicted mass losser
ror
std(error) = 5.1mg
Run
Model build with all data from three months including all maintenance procedures and process changes
Model works almost perfect
Model can be applied over maintenance activities
Model build with all data from three months including all maintenance procedures and process changes
Model works almost perfect
Model can be applied over maintenance activities
Process deviationProcess deviation
R=0.93
Model uses: SiF4 response, Si II 518 nm, Si II 545 nm
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 18 Copyright © Qimonda AG 2007 · All rights reserved.
Model with Continuously Updating Procedure
Continuously adaptation of modelparameters because of
• Maintenance activities
• Process changes
Prediction model build at every measurement of the actual mass loss including values from the last month
Continuously adaptation of modelparameters because of
• Maintenance activities
• Process changes
Prediction model build at every measurement of the actual mass loss including values from the last month
50 100 150 200 250 300100
110
120
130
140
150
160
170
50 100 150 200 250 300-1500
-1000
-500
0
500
1000
const
E_432_SIF4_ME22_A
E_518_SIII_ME21_A
E_545_SIII_ME31_A
const SiF4 response Si II 518 nm Si II 545 nmruns
mod
el p
aram
eter
sm
ass
loss
measured predicted
Process deviationProcess deviation
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 19 Copyright © Qimonda AG 2007 · All rights reserved.
Model with Continuously Updating Procedure
Significant improvement of prediction quality by adaptive adjustment of Model parameters
The predicted mass loss shows less error at process changes
Significant improvement of prediction quality by adaptive adjustment of Model parameters
The predicted mass loss shows less error at process changes
120 140 160
80
100
120
140
160
180
259
R=0.96
mas
s lo
ss
predicted mass loss50 100 150 200 250 300
-25
-20
-15
-10
-5
0
5
10
15
erro
r
std(error)=4.6mg
runs
Process deviationProcess deviation
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 20 Copyright © Qimonda AG 2007 · All rights reserved.
Offline AnalysisOffline Analysis
Outline of the PresentationScheme of Data Processing
Etch Process / Recording spectraEtch Process / Recording spectra
Plasma
Data Mining via Ridge Regression / define Areas
300 400 500 600 700-1000
-500
0
500
1000
1500
Preprocessing of OES Data
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
On-line Application of Model On-line Application of Model
Applying Model to APC Trend
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 21 Copyright © Qimonda AG 2007 · All rights reserved.
Connection to APC Trend
Formula predicting the mass loss had to put to APC Trend manually.
To be automated for roll out
Formula predicting the mass loss had to put to APC Trend manually.
To be automated for roll out
Va
lue
Time axis
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 22 Copyright © Qimonda AG 2007 · All rights reserved.
Possible Reactions on the Model Output
Actual implemented actions
• Model output at APC Trend
Email if mass loss out of spec
Further usage by engineers
Actual implemented actions
• Model output at APC Trend
Email if mass loss out of spec
Further usage by engineers
Not possible to implement
• Real time reaction during wafer processing to stop the process by endpoint detection
Variance of individual values too high, probability to create scrap
Not possible to implement
• Real time reaction during wafer processing to stop the process by endpoint detection
Variance of individual values too high, probability to create scrap
Future items to be checked
• Centering the process regarding his spec limits
• Adapting process steps after the deep trench etching
Could be automated using R2R control
Future items to be checked
• Centering the process regarding his spec limits
• Adapting process steps after the deep trench etching
Could be automated using R2R control
Thank you
The World’s LeadingCreative Memory Company
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