Relationship Between in-situ Information and ex-situ Metrology in Metal Etch

ProcessesJill Card, An Cao, Wai Chan, Bill Martin, Yi-Min Lai

IBEX Process Technology,A division of NeuMath, Inc

Outline

● BackgroundWhat we want for APCThe current situation in IC fabrication

● Project OverviewProduct designData collectionModel structure

● Results

Background

Ideal Semiconductor Fabrication:

Processes running on target

Continuous process monitoring and control at the tool level

Impending scrap events immediately detected and prevented

Advanced Fault Detection

Reliable Root Cause Analysis

Heads-up for tool failuresPinpoint problems and advise maintenance actions

High Yield by coordinating different steps and processes

Lot is Processed

1 Lot is Measured3

Lot Moves to Measurement Tool2

Data to Process Tool SPC Chart4

Chart “Violates”

5

Lot Goes on Hold Yellow Light On

6

MT Takes Action

Delay!

Current Fabrication Situation

Production line may be running for 5 lots with scraps before scraps are detected – at a cost of $$$ per lot.

Solution?

Lot is Measured3Lot is Processed

1

ex-situ data enhances the model

Chart “Violates”

5

Lot Moves to Meas. Tool2

Tool SPC Chart

NN ModelPredictedex-situ

In-situ data is readily available, no delays

The Proposal

SupposeWe can build a map between in-situ information and ex-situ metrology, then we can use in-situ data to predict the wafer quality directly, thereby avoiding the metrology delay.

Direct benefits Real time monitoring of wafer quality Predictions available for every single wafer Avoid delay in detection of major scrap events Take advantage of increasing availability of in-situ

data, e.g. sensor data. Potentially reduce ex-situ measurement cost

Experiments

We seek answers to these questions:

Can we accurately predict ex-situ information using in-situ results?

If yes, is there a relationship that can be easily interpreted?

Data Collection

● Production data from Metal Etch process4 months of data, total = 30K records. About 1.3K records have ex-situ information collected.

● Modeling one critical etch step

● Inputs includes feed-forward metrology information from the previous steps.

Neural Network (NN) Models

• Neural Network modeling was chosen because the relationship between in-situ and ex-situ metrology is hard to formulate mathematically.

• NN learns the rules from the dataset itself, no prior knowledge is required.

• IBEX Dynamic Neural Controller [commercial software package] was used.

• Separate neural network models are built for each ex-situ metrology measurement.

Model Inputs vs. OutputsTCP RF Forward PowerBias RF Forward Power

Temperature Upper SenseTemperature Bottom Electric SenseTemprature Turbo Manifold Sense

Tempature Vat Valve SenseChamber Pressure

Chamber Clamp PressureChamber ESC Voltage

TCP RF Reference PowerTCP Match Tune Cap

TCP Match Phase Error TCP Line ImpedanceTCP Match Load CapBias Match Load CapBias Match Tune Cap

Bias Match Peak VoltageBias RF Ref Power

Chamber Ref Manometer Pressure Chamber Pressure Valve Angle

Chamber Clamp FlowChamber End Point Channel AChamber End Point Channel B

Chamber ESC Current LeakDICD_MeanDICD_Std

Inter Layer Dielectric DepositionPost CMP Thickness

Post CMP Thickness NonuniformityPercent Open Area

DFT_DICD

OutputsFICD_meanFICD_std

FICD_slopeDefectDensity

Results

We sought to answer these questions:

1. Can we predict ex-situ information with in-situ results, accurately?

Yes!

2. If yes, is there an easily-determined relationship?

Model Accuracy

Note:Prior metrology is important!

Outputs Accuracy Records usedFICD_mean 0.53 1254FICD_std 0.92 1254

FICD_slope 0.93 1225DefectDensity 0.80 99

Prediction Fitting Curve

FICD_Mean

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

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Measured Predicted

Accuracy = 0.53, r2=0.95

Accuracy Depends on Limits Setting

Recipe 13

0.47

0.49

0.51

0.53

0.55

0.57

0 50 100 150 200 250 300

Measured Predicted Target_Lower_Limit

Target_Limit Target_Upper_Limit Soft_Upper_Limit

Accuracy = 0.95

Accuracy for A Different Recipe

FICD_Mean - Recipe 9

0.38

0.43

0.48

0.53

0.58

0 10 20 30 40 50

Measured Predicted Target_Lower_Limit

Target_Limit Target_Upper_Limit Soft_Upper_Limit

Accuracy = 0.61

Prediction Fitting Curve

FICD_Slope

83

84

85

86

87

88

89

90

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Measured Predicted

Accuracy = 0.93

Prediction Fitting Curve

FICD_STD

0.000

0.005

0.010

0.015

0.020

0.025

0.030

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Measured Predicted

Accuracy = 0.92

Prediction Fitting Curve

DefectDensity

0

1

2

3

4

5

6

7

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Measured Predicted

Accuracy = 0.80. Limited number of observed records may affect the model accuracy.

Sensitivity Analysis

We sought to answer these questions:

Can we predict ex-situ information with in-situ results, accurately?

Yes! We successfully predicted ex-situ metrology from the in-situ metrology with reasonable accuracy (ranging from 0.5 to 0.9)

If yes, is there an easily-determined relationship?

No. It requires Sensitivity Analysis.

Bias Match VoltageDICD Mean

Temp Turbo Manifold Sensor

Sensitivity Analysis

Recipe 1Complicated relationship.

FICD depends on multiple inputs

Temp Turbo Manifold Sensor

DICD Mean

Temp Turbo Manifold Sensor

Sensitivity Analysis

Recipe 2Sensitivity is also recipe dependent

Temp Turbo Manifold Sensor

Sensitivity Analysis

Recipe 2

Other ex-situ metrologies show similar complicated sensitivity curves. An example, FICD Slope, is shown.

Sensitivity of ex-situ metrology

Ex-situ metrology depends on complicated interactions among the trace inputs and the feed forward metrology.

Recipe-dependenceNon-linear sensitivity curvesPossible dependence on tool health situation Sensitivity changes over time

This demands an intelligent algorithm for better interpretation.

Output Dependency on InputsVariables FICD_mean FICD_std FICD_slope DefectDensity

TCP RF Forward PowerBias RF Forward Power

Temperature Upper Sense XTemperature Bottom Electric Sense XTemprature Turbo Manifold Sense X X X X

Tempature Vat Valve Sense XChamber Pressure X

Chamber Clamp PressureChamber ESC Voltage X

TCP RF Reference PowerTCP Match Tune Cap X

TCP Match Phase Error XTCP Line ImpedanceTCP Match Load Cap XBias Match Load Cap XBias Match Tune Cap X

Bias Match Peak Voltage X X X XBias RF Ref Power X X

Chamber Ref Manometer Pressure XChamber Pressure Valve Angle

Chamber Clamp FlowChamber End Point Channel A XChamber End Point Channel B X

Chamber ESC Current Leak X XDICD_Mean X X X XDICD_Std X

Inter Layer Dielectric Deposition X XPost CMP Thickness X X

Post CMP Thickness Nonuniformity X XPercent Open Area X X

DFT_DICD X X X

Summary

● Our previous work** shows comprehensive root cause analysis through neural model of all metrology outputs (in-situ and ex-situ) and controllable variable inputs.

● Recommends corrective action Wafer to Wafer

maintenance actions setpointed recipe parameters.

** Card, et. al. Fab Process And Equipment Performance Improvement After An Advanced Process Controller Installation, AEC/APC-Europe 2004

Etch RateUniformitySelectivityParticles

Valve AngleHe Clamp FlowWafer Area Pres.

Gas flowPressureTemp

Conditioning RunWet CleanReplace MFCReplace QuartzReplace ChuckHGSReplace vat valve

Ex-situ

In-situ

Recommended optimalRepair or Recipe adjustment

Next Steps

● By prediction of ex-situ measures with precision, DNC can provide root cause analysis for tool health and process health without reliance on ex-situ measures.

● Addition of more complex sensors (RF probe, OES) may well add the remaining information content to complete ex-situ characterization

Valve AngleHe Clamp FlowWafer Area Pres.

Gas flowPressureTemp

Conditioning RunWet CleanReplace MFCReplace QuartzReplace ChuckHGSReplace vat valve

In-situ

Recommended optimalRepair or Recipe adjustment

OESRF Probe

Conclusion

Accurate predictions of ex-situ metrology can be achieved from in-situ information only.

Next StepsIntroduce root cause tool control algorithm for maintenance

and recipe parameter response.Continue evaluation of complex sensors to further enhance

ex-situ metrology prediction using in-situ sources only.

Sensitivity analysisComplex relationship to ex-situ metrology. However, if

information present, root cause optimization can follow with no loss of precision.