Virtual Metrology to Measure Mass Loss at Deep Trench Processes

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


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


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









Preprocessing of OES Data300 400 500 600 700

-1000

-500

0

500

1000

1500Etch Process / Recording spectraEtch Process / Recording spectra

Plasma


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


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








Preprocessing of OES Data


Plasma


300 400 500 600 700-1000

-500

0

500

1000

1500


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


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:


• 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


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

)









Plasma


300 400 500 600 700-1000

-500

0

500

1000

1500



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



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







Plasma


300 400 500 600 700-1000

-500

0

500

1000

1500





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:


• 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:


• 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


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


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



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






Plasma


300 400 500 600 700-1000

-500

0

500

1000

1500







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


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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Virtual Metrology to Measure Mass Loss at Deep Trench Processes

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