Virtual metrology to measure mass loss at deep trench processes
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Virtual Metrology to Measure Mass Loss at Deep Trench Processes. April 18th, 2007. Tilo Wünsche, Matthias Rudolph, Jan Zimpel (adp GmbH). Motivation to Measure the Mass Loss during the Deep Trench Etch Process. DT etch. hard mask. hard mask. D m Si. Si. Si.

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Virtual Metrology to Measure Mass Loss at Deep Trench Processes

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Virtual metrology to measure mass loss at deep trench processes

Virtual Metrology to Measure Mass Loss at Deep Trench Processes

April 18th, 2007

Tilo Wünsche, Matthias Rudolph, Jan Zimpel (adp GmbH)


Motivation to measure the mass loss during the deep trench etch process

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


Motivation to measure the mass loss during the deep trench etch process1

Virtual Metrology predicting Mass loss

Value for each wafer (high sample rate)

Little cycle time consumption

Motivation to Measure the Mass Loss during the Deep Trench Etch Process

  • 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

Capacity of DRAM

mass loss

Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 3


Outline of the presentation scheme of data processing

Plasma

Outline of the PresentationScheme of Data Processing

Etch Process / Recording spectra

Offline Analysis

Data Mining via Ridge Regression / define Areas

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


Outline of the presentation scheme of data processing1

Plasma

Outline of the PresentationScheme of Data Processing

Etch Process / Recording spectra

Offline Analysis

Data Mining via Ridge Regression / define Areas

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


Measure and processing oes data sensor integration

Plasma

MID: XYZSLOT: 1Recipe: ABC

Measure and Processing OES DataSensor Integration

OES Sensor

Equipment

HOST

optical fiber

FAB LAN

recipe start with logistic (MID, Slot, Wafer, Recipe) recipe stop, recipe step

OES Data

OES Measurement Application

OES Analysis Application

FDC Application

  • Spectral data visualization

  • Data mining (PCA, Modeling)

  • EP model design

  • On-line process monitoring

  • Visualization of process indicators

  • OCAP

SpectralDatabase

Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 6


Information of the oes spectrum

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


Outline of the presentation scheme of data processing2

Plasma

Outline of the PresentationScheme of Data Processing

Etch Process / Recording spectra

Offline Analysis

Data Mining via Ridge Regression / define Areas

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


Data mining task

Data Mining – Task

High dimensional data cube of OES spectra containing information

mass loss

etch time t

wafer 1 ... 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


Data mining ridge regression i

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

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


Data mining ridge regression ii

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


Outline of the presentation scheme of data processing3

Plasma

Outline of the PresentationScheme of Data Processing

Etch Process / Recording spectra

Offline Analysis

Data Mining via Ridge Regression / define Areas

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


Long term process effects

Process deviation

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

runs

Maintenance activities

Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 13


Preprocessing of oes data

Preprocessing of OES Data

  • Normalization to step over wet clean

  • cycles

  • Best results by normalization on baseof total intensity of the actual measured spectrum (e.g. integral or norm)

HBr Response

1.0

16000

0.8

15000

0.6

14000

0.4

0.2

13000

0.0

12000

-0.2

  • Data filtering to exclude

  • Measurement failures

  • Bad processes

    • Only real outliers should be removed

    • Distribution function of predictors and response have to be kept

11000

-0.4

10000

-0.6

9000

-0.8

8000

-1.0

20

40

60

80

100

120

140

160

Increase of intensity after wet clean

Outliers

Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 14


Outline of the presentation scheme of data processing4

Plasma

Outline of the PresentationScheme of Data Processing

Etch Process / Recording spectra

Offline Analysis

Data Mining via Ridge Regression / define Areas

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


Forward regression

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)

  • 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


Static model

Static Model

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


Model with continuously updating procedure

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


Model with continuously updating procedure1

Model with Continuously Updating Procedure

mass loss

error

R=0.96

Process deviation

std(error)=4.6mg

runs

predicted mass loss

  • 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


Outline of the presentation scheme of data processing5

Plasma

Outline of the PresentationScheme of Data Processing

Etch Process / Recording spectra

Offline Analysis

Data Mining via Ridge Regression / define Areas

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


Connection to apc trend

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


Possible reactions on the model output

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

  • 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


Virtual metrology to measure mass loss at deep trench processes

Thank you

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