1. Virtual Metrology to Measure Mass Loss at Deep Trench Processes
April 18th, 2007
Tilo Wünsche, Matthias Rudolph, Jan Zimpel (adp GmbH)

2. Motivation to Measure the Mass Loss during the Deep Trench Etch Process
DT etch
hard
mask
hard
mask
DmSi Si Si
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
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 2

3. Motivation to Measure the Mass Loss during the Deep Trench Etch Process
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
Capacity of DRAM
mass loss
Virtual Metrology predicting Mass loss
Value for each wafer (high sample rate)
Little cycle time consumption
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 3

4. Outline of the Presentation Scheme of Data Processing
Etch Process / Recording spectra
Offline Analysis
Data Mining via Ridge Regression / define Areas
Plasma
Preprocessing of OES Data
On-line Application of Model
Applying Model to APC Trend
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 4

5. Outline of the Presentation Scheme of Data Processing
Etch Process / Recording spectra
Offline Analysis
Data Mining via Ridge Regression / define Areas
Plasma
Preprocessing of OES Data
On-line Application of Model
Applying Model to APC Trend
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 5

6. Measure and Processing OES Data Sensor Integration
OES Sensor
Equipment
HOST
Plasma
optical fiber
FAB LAN
OES Data
recipe start with logistic (MID, Slot, Wafer, Recipe) recipe stop, recipe step
OES Measurement Application
OES Analysis Application
FDC Application
Spectral data visualization
Data mining (PCA, Modeling)
EP model design
On-line process monitoring
MID: XYZ SLOT: 1 Recipe: ABC
Visualization of process indicators
OCAP
Spectral Database
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 6

7. Information of the OES Spectrum
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
Break Through
Main Etch
Response from NF3
Response from SiF4
Response from HBr
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 7

8. Outline of the Presentation Scheme of Data Processing
Etch Process / Recording spectra
Offline Analysis
Data Mining via Ridge Regression / define Areas
Plasma
Preprocessing of OES Data
On-line Application of Model
Applying Model to APC Trend
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 8

9. Data Mining – Task
High dimensional data cube of OES spectra containing information
mass loss
etch time t
wafer N
wavelength 
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
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 9

10. Data Mining – Ridge Regression – I
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
Step1
Step2
Step3
426 /440nm
wavelength (nm)
519 nm
548 nm
657 nm
time /s
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 10

11. Data Mining – Ridge Regression – II
wavelength (nm)
mass loss
time (s)
Whole response pattern could be used as a model
predicted mass loss
To predict the mass loss model some significant spectral ranges are sufficient.
Simple automated updated procedure possible
Robust model
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 11

12. Outline of the Presentation Scheme of Data Processing
Etch Process / Recording spectra
Offline Analysis
Data Mining via Ridge Regression / define Areas
Plasma
Preprocessing of OES Data
On-line Application of Model
Applying Model to APC Trend
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 12

13. 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
mass loss
Process deviation
runs
Maintenance activities
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 13

14. Preprocessing of OES Data
HBr Response
Normalization to step over wet clean
cycles
Best results by normalization on base of total intensity of the actual measured spectrum (e.g. integral or norm)
1.0
16000
0.8
15000
0.6
14000
0.4
13000
0.2
12000
0.0
-0.2
11000
Data filtering to exclude
Measurement failures
Bad processes
Only real outliers should be removed
Distribution function of predictors and response have to be kept
-0.4
10000
-0.6
9000
-0.8
8000
-1.0 20 40 60 80
100
120
140
160
Outliers
Increase of intensity after wet clean
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 14

15. Outline of the Presentation Scheme of Data Processing
Etch Process / Recording spectra
Offline Analysis
Data Mining via Ridge Regression / define Areas
Plasma
Preprocessing of OES Data
On-line Application of Model
Applying Model to APC Trend
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 15

16. Forward Regression Yes No 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)
Search for best correlating predictor
p-value
Check if correlation has highly probability
Ypre = A*x + B*x
Add predictor to the model
Err = Ypre - Yact
Apply model and calculate residual error
Yes No
Finished
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 16

17. Static Model Process deviation mass loss R=0.93 error
Model uses: SiF4 response, Si II 518 nm, Si II 545 nm
Process deviation
mass loss
R=0.93
error
std(error) = 5.1mg
predicted mass loss
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
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 17

18. Model with Continuously Updating Procedure
Process deviation
Continuously adaptation of model
parameters because of
Maintenance activities
Process changes
Prediction model build at every measurement of the actual mass loss including values from the last month
mass loss
measured
predicted
model parameters
const
SiF4 response
Si II 518 nm
Si II 545 nm
runs
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 18

19. Model with Continuously Updating Procedure
mass loss
error
R=0.96
Process deviation
std(error)=4.6mg
predicted mass loss
runs
Significant improvement of prediction quality by adaptive adjustment of Model parameters
The predicted mass loss shows less error at process changes
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 19

20. Outline of the Presentation Scheme of Data Processing
Etch Process / Recording spectra
Offline Analysis
Data Mining via Ridge Regression / define Areas
Plasma
Preprocessing of OES Data
On-line Application of Model
Applying Model to APC Trend
Building Model via Forward Regression
DTml_pred = f(OES_Areas)
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 20

21. Connection to APC Trend
Formula predicting the mass loss had to put to APC Trend manually.
To be automated for roll out
Value
Time axis
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 21

22. Possible Reactions on the Model Output
Actual implemented actions
Model output at APC Trend
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
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
Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 22

23. Thank you
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