1. 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

2. Outline ● Background What we want for APC The current situation in IC fabrication ● Project Overview Product design Data collection Model structure ● Results

3. 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

4. Lot is Processed 1 Lot is Measured 3 Lot Moves to Measurement Tool 2 Data to Process Tool SPC Chart 4 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.

5. Solution? Lot is Measured 3 Lot is Processed 1 ex-situ data enhances the model Chart “Violates” 5 Lot Moves to Meas. Tool 2 Tool SPC Chart NN Model Predicted ex-situ In-situ data is readily available, no delays

6. 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

7. 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?

8. 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.

9. 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.

10. Model Inputs vs. Outputs

11. 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?

12. Model Accuracy Note: Prior metrology is important!

13. Prediction Fitting Curve Accuracy = 0.53, r 2 =0.95

14. Accuracy Depends on Limits Setting Accuracy = 0.95

15. Accuracy for A Different Recipe Accuracy = 0.61

16. Prediction Fitting Curve Accuracy = 0.93

17. Prediction Fitting Curve Accuracy = 0.92

18. Prediction Fitting Curve Accuracy = 0.80. Limited number of observed records may affect the model accuracy.

19. 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.

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

21. DICD Mean Temp Turbo Manifold Sensor Sensitivity Analysis Recipe 2 Sensitivity is also recipe dependent Temp Turbo Manifold Sensor

22. Sensitivity Analysis Recipe 2 Other ex-situ metrologies show similar complicated sensitivity curves. An example, FICD Slope, is shown.

23. 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 S ensitivity changes over time This demands an intelligent algorithm for better interpretation.

24. Output Dependency on Inputs

25. 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

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

27. 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

28. Valve Angle He Clamp Flow Wafer Area Pres. Gas flow Pressure Temp Conditioning Run Wet Clean Replace MFC Replace Quartz Replace Chuck HGS Replace vat valve In-situ Recommended optimal Repair or Recipe adjustment OES RF Probe

29. 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.