Chamber Matching using an MVA approach in Dry ...

Chamber Matching using an MVA approach in Dry Etch and CVD Carlo BEVILACQUA, Giuseppe FAZIO, Matteo GALBIATI, Raffaele NINNI (STMicroelectronics) Stela DIAMANT (MKS Instruments)

Motivation

Many APC methodologies are used by engineers to create robust process qualification and to speed up transfer to manufacturing. The most important relates to chamber/tool matching. Currently we are using univariate analysis techniques but for real-world situations they are neither simple to use, fast to execute nor robust. This provides the motivation to introduce a multivariate analysis (MVA) approach.

Solution 1/3

The MVA approach is based on the assumption that the process parameters (variables) are usually not independent and that consequently a better result will be obtained by using the entire observation set, rather than just a summary. The commonly used MVA methods are PCA, PLS and PLS-DA.

Solution 2/3 Two Levels of Modeling Observation level : Evaluating the individual observation (dynamic process information) i.e., time points, Predicting wafer maturity
Modeling the evolution of wafers in the training set
Building control charts from the training set
Monitoring the evolution of new wafers Batch level : Modeling the whole completed wafers i.e., initial conditions, wafer process variables and dynamics, end result and quality characteristics are used for making a model over the whole wafer, classify new wafer quality and understand how quality is influenced by the combination of initial conditions and wafer evolution.
Modeling the training set
Predicting results or classifying whole new wafers
Virtual metrology - wafer acceptance test

Solution 3/3 Multivariate Analysis Hotelling’s T2
Summarizes statistical deviations per wafer
Model population is defined by T2critical
The larger T2 is, the less likely it is to belong to the model DModX (residual error)
Large DModX indicates a change from the correlations existent in the original model.
DModX indicates when the model is not appropriate Principal Component Scatter Plot
Scatter plot (t1/t2) is a window into variable space, providing information about outliers, trends, and similarity of data
The number of principal components is a function of how well the model explains the systematic variance of the data Contribution Plot
List of the VID’s, which are contributing most to the process results / problems
When sensors are used, identifies their most important parameters

Results and Discussion

We are applying a technique called "Batch analysis MVA”, based on two steps:

First Step •PLS is modeling each recipe step separated •We get the same amount of independent PLS models as recipe steps

Second Step •We use the results (scores) of the PLS models as new input •A PCA will be applied to catch all recipe steps together

CVD project

30 40 50 60 tPS[1] 70 80 90 100

Chamber A

110 120 130 140

Class 1 Class 2

150

t2 t3

SIMCA-P+ 11.5 - 12/5/2006 4:12:54 PM

DxZ1B_SIN01_GPC_DATA sd - batch level (scores).M1 (PCA-Class(1)), PS-DxZ1B_SIN01_GPC_DATA sd - batch level (scores) Score ContribPS(Group 2 - Group 1), Weight=p[1]p[2]

t1

Var ID (Primary)

Contribution plot differences

80

60

120

100

0

40

20

-20

-40

-60

-80

-100

-120

This are the scores based on the lower level raw data

DxZ1B_SIN01_GPC_DATA sd.M1:-1 + 1 + 2 + 3 + 4 + 5 + 6 + -2, PS-DxZ1B_SIN01_GPC_DATA sd Score Contrib PS Aligned(Group 2 - Group 1), Weight=p[2]p[3]

throttle valve step

t1 t2 t3 of LL used as variables for UL model

2

SIMCA-P+ 11.5 - 12/6/2006 5:39:16 PM

reflected pwr

Throttle valve step is the most contributing variable

Var ID (Primary) forward pwr

Results and Discussion

20

AFC current flow N2

t[2]_-1 + t[2]_-1 + t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + + t[2]_-1 t[3]_-1 + + t[3]_-1 t[3]_-1 + t[3]_-1 + + t[3]_-1 t[3]_-1 + t[3]_-1 + + t[3]_-1 t[3]_-1 + t[3]_-1 + t[3]_-1 + + t[3]_-1 t[3]_-1 + t[3]_-1 + + t[3]_-1 t[3]_-1 + t[3]_-1 + + t[3]_-1 t[3]_-1 + t[3]_-1 + t[3]_-1 + + t[3]_-1 t[3]_-1 + + t[3]_-1 t[3]_-1 t[3]_-1 + + t[3]_-1 + + t[3]_-1 t[3]_-1 + t[3]_-1 + t[3]_-1 + + t[3]_-1 t[3]_-1 + + t[3]_-1 t[3]_-1 + t[3]_-1 + + t[3]_-1 t[3]_-1 + t[3]_-1 + t[3]_-1 + t[3]_-1 + + t[3]_-1 t[3]_-1 + + t[3]_-1 t[3]_-1 + t[3]_-1 + + t[3]_-1 t[3]_-1 + t[3]_-1 + + t[3]_-1 t[3]_-1 + + t[3]_-1 t[3]_-1 + + t[3]_-1 t[3]_-1 +

Description

Chamber B

10

1

0

-1

-2 AFC current flow NH3

Process: DAMASCENE NITRIDE deposition Equipment: Applied Materials CENTURA platform

0

Score ContribPS(Group 2 - Group 1), Weight=p2p3

AFC c urrent flow SiH4-Low

Problem: Defect reduction

DxZ1B_SIN01_GPC_DATA sd - batch level (scores).M1 (PCA-Class(1)), PS-DxZ1B_SIN01_GPC_DATA sd - batch level (scores) tPS[Comp. 1]/tPS[Comp. 2] Colored according to classes in M1

-10

MV Analysis

PCA-class model

80

70

60

50

40

30

0

20

10

-10

-20

SIMCA-P+ 11.5 - 12/5/2006 4:11:53 PM

R2X[1] = 0.255096on the differences R2X[2] = 0.138397between the two chambers The model is build based Ellipse: Hotelling T2PS (0.95)

Score ContribPS(Group 2 - Group 1), Weight=p1p2

Duration_t[1]_-1 t[1]_-1 + + t[1]_-1 + + t[1]_-1 t[1]_-1 + + t[1]_-1 t[1]_-1 + t[1]_-1 + + t[1]_-1 t[1]_-1 + + t[1]_-1 t[1]_-1 + t[1]_-1 + + t[1]_-1 t[1]_-1 + t[1]_-1 t[1]_-1 + + t[1]_-1 + + t[1]_-1 t[1]_-1 + + t[1]_-1 t[1]_-1 + t[1]_-1 + + t[1]_-1 t[1]_-1 + t[1]_-1 t[1]_-1 + + t[1]_-1 + t[1]_-1 + t[1]_-1 + + t[1]_-1 t[1]_-1 + t[1]_-1 + + t[1]_-1 t[1]_-1 + t[1]_-1 + t[1]_-1 + + t[1]_-1 t[1]_-1 + t[1]_-1 + + t[1]_-1 t[1]_-1 + + t[1]_-1 t[1]_-1 + t[1]_-1 + t[1]_-1 + t[1]_-1 + + t[1]_-1 t[1]_-1 + t[1]_-1 + + t[1]_-1 t[1]_-1 + + t[1]_-1 t[1]_-1 + t[1]_-1 + + t[2]_-1 t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + + t[2]_-1 t[2]_-1 + + t[2]_-1 t[2]_-1 + t[2]_-1 + t[2]_-1 + t[2]_-1 + + t[2]_-1

It is very difficult to isolate root cause by using the univariate method; as a result, analysis and study take a long time and the outcome is often not acceptable.

tPS[2]

Results and Discussion CVD project

Hypothesis Chamber manometer bad calibration: Throttle Valve position is related to pressure read by chamber manometer A bad chamber manometer read could set a different Throttle Valve position to reach the requested pressure By analyzing dataset, we can suppose a difference in pressure reading between the two chambers of about 1.5 Torr Test Changing pressure setpoint By changing pressure setpoint in chamber A we reached the same Throttle Valve position as in chamber B, which is the referencing chamber Defect has been measured in all these pressure configuration

Results and Discussion CVD project

Test Result (Defect value by aligning throttle valve positions) By changing pressure setpoint we obtain different defect values and the more chamber A throttle valve position is near to chamber B throttle valve position, the more we get a good defect result As result, the amount of bumps on flat wafer decreases by a factor 4 (the circumference radius on which bumps saturate is double) by changing setpoint pressure in chamber A of 1.7 Torr The inspected area which gives a saturated defect value on standard condition is painted in orange

Standard condition

The inspected area painted in orange which gives a saturated defect value on modified condition, with aligned throttle valve position between chambers is larger

Throttle valve position aligned

Results and Discussion Description Process: polysilicon etch Equipment: LAM Research ALLIANCE platform

Target: fine tuning Applying MVA for chamber matching activity one can fine tune processes on different chambers and unfold the relationship among the underlying parameters. MV Analysis

Score contribution plot PM2with PM4 - batch level (scores).M1 (PCA-X), PS-PM2with PM4 - batch level (scores ) Score ContribPS(Obs Group - Obs Group), Weight=p[1]p[2]

PCA model PM2with PM4 - batch level (scores).M1 (PCA-X), PS-PM2with PM4 - batch level (scores ) tPS[Comp. 1]/tPS[Comp. 2] Colored according to Obs ID (Primary)

PM2* PM4*

Score ContribPS(Obs Group- Obs Group), Weight=p1p2

60

40

20

0

-20

-40

-60

-80

1000

10

9

7 8

5 6

3 4

2

0

1

0

-100

2000

3000 SI MCA-P+ 11 - 3/19/ 2007 3:41:46 PM

Contribution plot based on the difference between the two chambers

-100 tPS[2]

P M 2w ith P M 4 .M 3: 2, P S -P M 2w ith P M 4 S c o re Co nt rib P S A lig ne d(Grou p - G ro up ), W eig ht = p[4 ]

-200

150

160

SIMCA-P+ 11 - 3/19/2007 3:40:41 PM

170

180

0

- 100

V ar ID (Primary ) S I MC A -P + 1 1 - 3 / 19 / 2 0 0 7 3: 4 3 : 1 7 P M

Chamber Pressure in

140

Vacuum line Pressure

130

Top RF Power

120

Pressure (10 mTorr)

110

Top Matching Tune Ca

100

Top RF Reflected Pow

90

Bias Voltage

80

Bottom Matching Tune

70

Ellipse: Ho telling T2PS (0.95)

Top Matching Load Ca

60

Bottom Matching Load

50

Holding Voltage

40 tPS[1]

He Backside flow

30

He

20

CF4

10

CH2F2

0

R2X[2] = 0.0822417

HBr

-10

O2 (small MFC)

-20

He Backside pressure

-30

ESC Potential

-40

Leak Current on Chuc

-50

Helium Backside Flow

-60

R2X[1] = 0.3434

Chamber Pressure

-70

Electrode - Lower te

-80

Bottom RF Reflected

-90

Chamber middle temp.

-100

VAT Position

-300

Score ContribPS(Group - Group), Weight=p4

100

Bottom RF Power

DryEtch project

Results and Discussion DryEtch project

Different in a contributing parameter  Data Collection performed during production is able to collect more than 50 process and chamber parameters  MVA allows to discriminate which parameters are misaligned between the different chambers

PM2PM4-DA.M9:8 Holding Voltage (Aligned)

-300

PM2

-400

PM2PM4-DA.M9:8 ESC Potential (Aligned) -500 800 -600

790

PM4

780

PM4

-700

770

PM2PM4-DA.M9:8 Bottom Matching Tune Capacitor Position (Aligned)

760 -800 750

0

10

20

30

40

50

60 740

70

80

90

100

110

120

130

140

15010000 160

170

$Time (normalized) SIMCA-P+ 11 - 3/19/2007 4:10: 38 PM

730

9000 720

PM2

710

8000

700

7000 0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

6000(normalized) $Time SIMCA-P+ 11 - 3/19/2007 4:11: 31 PM

5000

4000

3000 0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

$Time (normalized) SIMCA-P+ 11 - 3/19/ 2007 4:12: 11 PM

170

Results and Discussion DryEtch project

Hardware Analysis  Analyzing temporal trends of the selected parameters, found some misalignments between one chamber and the others in ESC configurations  it is possible to act on the ESC Power Supply to correct the misalignments

 Possible benefits on chamber process results: CD and End Point distributions, etc..