relationship between in-situ information and ex-situ metrology in metal etch processes
DESCRIPTION
Relationship Between in-situ Information and ex-situ Metrology in Metal Etch Processes. Jill Card, An Cao, Wai Chan, Bill Martin, Yi-Min Lai IBEX Process Technology, A division of NeuMath, Inc. Outline. Background What we want for APC The current situation in IC fabrication - PowerPoint PPT PresentationTRANSCRIPT
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
12/25/03 01/14/04 02/03/04 02/23/04 03/14/04 04/03/04 04/23/04 05/13/04
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
12/25/03 01/14/04 02/03/04 02/23/04 03/14/04 04/03/04 04/23/04 05/13/04
Measured Predicted
Accuracy = 0.93
Prediction Fitting Curve
FICD_STD
0.000
0.005
0.010
0.015
0.020
0.025
0.030
12/25/03 01/14/04 02/03/04 02/23/04 03/14/04 04/03/04 04/23/04 05/13/04
Measured Predicted
Accuracy = 0.92
Prediction Fitting Curve
DefectDensity
0
1
2
3
4
5
6
7
12/25/03 01/14/04 02/03/04 02/23/04 03/14/04 04/03/04 04/23/04 05/13/04
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.