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

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. 5 Chart “Violates” Lot Goes on Hold 6 Yellow Light On MT Takes Action Lot Moves to Measurement Tool 2 3 1 Lot is Measured Lot is Processed Current Fabrication Situation 4 Tool SPC Chart Data to Process Delay! • Production line may be running for • 5 lots with scraps before scraps are detected • – at a cost of $$$ per lot.

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

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, r2=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 Temp Turbo Manifold Sensor Sensitivity Analysis Recipe 1 Complicated relationship. FICD depends on multiple inputs

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

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

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