Relationship_betwee_in-situ_and_ex-situ_metrology

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					   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

●   Project Overview
     Product design
     Data collection
     Model 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 failures
     Pinpoint problems and advise maintenance actions
  High Yield by coordinating different steps and processes
Current Fabrication Situation
                                        5
                                         Chart “Violates”


6 Lot Goes on Hold                           4
  Yellow Light On                                                Tool SPC Chart
                                            Data to Process
  MT Takes Action



                             2 Lot Moves to Measurement Tool



                                                 Delay!

            1                                                   3 Lot is Measured
         Lot is
         Processed   Production line may be running for
                     5 lots with scraps before scraps are detected
                     – at a cost of $$$ per lot.
 Solution?
                                                  5
                                                   Chart “Violates”

                                                Tool SPC Chart

In-situ data is readily                                     ex-situ data enhances
available, no delays                                         the model

                      NN Model
                          Predicted
                           ex-situ



                               2 Lot Moves to Meas. Tool
            1
         Lot is                                                       3 Lot is Measured
         Processed
The Proposal
Suppose
  We 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 process
     4 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. Outputs
            TCP RF Forward Power
            Bias RF Forward Power
          Temperature Upper Sense
     Temperature Bottom Electric Sense
      Temprature Turbo Manifold Sense
          Tempature Vat Valve Sense
               Chamber Pressure
           Chamber Clamp Pressure
             Chamber ESC Voltage
           TCP RF Reference Power
             TCP Match Tune Cap
            TCP Match Phase Error
              TCP Line Impedance
                                              Outputs
             TCP Match Load Cap             FICD_mean
             Bias Match Load Cap             FICD_std
             Bias Match Tune Cap
           Bias Match Peak Voltage
                                            FICD_slope
               Bias RF Ref Power           DefectDensity
     Chamber Ref Manometer Pressure
        Chamber Pressure Valve Angle
             Chamber Clamp Flow
        Chamber End Point Channel A
        Chamber End Point Channel B
          Chamber ESC Current Leak
                  DICD_Mean
                   DICD_Std
       Inter Layer Dielectric Deposition
             Post CMP Thickness
     Post CMP Thickness Nonuniformity
               Percent Open Area
                   DFT_DICD
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

     Outputs    Accuracy Records used
   FICD_mean      0.53       1254
    FICD_std      0.92       1254
   FICD_slope     0.93       1225
  DefectDensity   0.80        99
                Note:
      Prior metrology is important!
Prediction Fitting Curve

                                     FICD_Mean
  0.70
  0.65
  0.60
  0.55
  0.50
  0.45
  0.40
  0.35
  0.30
    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

0.57

0.55

0.53

0.51

0.49

0.47
       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

0.58

0.53

0.48

0.43

0.38
       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
90
89
88
87
86
85
84
83
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
0.030
0.025
0.020
0.015
0.010
0.005
0.000
   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
 7
 6
 5
 4
 3
 2
 1
 0
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.
Sensitivity Analysis

                                                  Recipe 1
          Bias Match Voltage                      Complicated
                                                  relationship.
                                DICD Mean
                                                  FICD depends
                                                  on multiple
                                                  inputs
                     Temp Turbo Manifold Sensor
    Temp Turbo Manifold Sensor
Sensitivity Analysis

                                                  Recipe 2
                                                  Sensitivity is
                                                  also recipe
                DICD Mean                         dependent

                              Temp Turbo Manifold Sensor
     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-dependence
    Non-linear sensitivity curves
    Possible dependence on tool health situation
    Sensitivity changes over time

This demands an intelligent algorithm for
  better interpretation.
Output Dependency on Inputs
                   Variables             FICD_mean FICD_std FICD_slope DefectDensity
            TCP RF Forward Power
            Bias RF Forward Power
          Temperature Upper Sense                                            X
     Temperature Bottom Electric Sense                X
      Temprature Turbo Manifold Sense        X        X         X            X
          Tempature Vat Valve Sense                                          X
               Chamber Pressure                                 X
           Chamber Clamp Pressure
             Chamber ESC Voltage                      X
           TCP RF Reference Power
             TCP Match Tune Cap                       X
            TCP Match Phase Error                                            X
              TCP Line Impedance
             TCP Match Load Cap                       X
             Bias Match Load Cap                                             X
             Bias Match Tune Cap                                             X
           Bias Match Peak Voltage           X        X         X            X
               Bias RF Ref Power                      X                      X
     Chamber Ref Manometer Pressure                                          X
        Chamber Pressure Valve Angle
             Chamber Clamp Flow
        Chamber End Point Channel A                   X
        Chamber End Point Channel B                                          X
          Chamber ESC Current Leak                    X                      X
                  DICD_Mean                  X        X         X            X
                   DICD_Std                           X
       Inter Layer Dielectric Deposition              X                      X
             Post CMP Thickness                       X         X
     Post CMP Thickness Nonuniformity                 X                      X
               Percent 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
                    Recommended optimal
                    Repair or Recipe adjustment



 Gas flow                                         Etch Rate
 Pressure                                         Uniformity         Ex-situ
 Temp                                             Selectivity
                                                  Particles
Conditioning Run
Wet Clean
Replace MFC                                       Valve Angle
Replace Quartz                                    He Clamp Flow
Replace Chuck                                                        In-situ
                                                  Wafer Area Pres.
HGS
Replace vat valve
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
                    Recommended optimal
                    Repair or Recipe adjustment



 Gas flow                                         Valve Angle
 Pressure                                         He Clamp Flow
 Temp                                             Wafer Area Pres.
Conditioning Run
Wet Clean                                                            In-situ
Replace MFC
Replace Quartz                                    OES
Replace Chuck                                     RF Probe
HGS
Replace vat valve
Conclusion

Accurate predictions of ex-situ metrology can be achieved
  from in-situ information only.
Next Steps
 Introduce 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 analysis
  Complex relationship to ex-situ metrology. However, if
    information present, root cause optimization can follow
    with no loss of precision.

				
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