Scalable Hierarchical Yield Control System For Semiconductor Manufacturing A Feasibility Study

1. Scalable Hierarchical Yield Control System For Semiconductor ManufacturingA Feasibility Study Bill Martin, Jill Card, Wai Chan, Joyce Hyde, Yi-Min Lai IBEX Process Technology A Division of Neumath, Inc., Haverhill, MA John Doxsey, Paul Fearon National Semiconductor, S. Portland ME

2. Outline • Overview • Design Approach • Feasibility Study Data Collection • Feasibility Study Results • Conclusions/Next Steps

3. Overview • Adaptive Hierarchical Design Works in conjunction with local tool controllers such as Neumath's Dynamic Neural Controller (DNC) product. • Optimises overall yield and end-of-line performance characteristics • Works with partial data to permit adaptive adjustment of downstream operation quality targets to minimize scrap

4. Yield Controller Hierarchical Design Y I E L D CONTROLLER Optimize Metrology Targets across Products and Process Steps X X DNC Deposition DNC CMP DNC Photo DNC Etch Dep Quality Metrics Photo Quality Metrics Etch Quality Metrics CMP Quality Metrics

5. Comprehensive Software Tool Development • Yield Control Layer • Accurate prediction of End Of Line (EOL) metrics • Automatic metrology target and spec adjustment • Tool Control Layer • Accurate prediction of post-process metrology and action advisory (DNCs, DNCe) • Automated recipe parameter target and spec adjustment

6. Additional Benefits • Full use of all in-situ and ex-situ sensors across products. • Automatic optimization of recipe parameters and metrology specifications. No setup required. • Determination of quantified sensor importance • Pinpointing troubled tools • Quantified impact on EOL metrics and Yield • W2W Detection, Diagnosis, and Fix.

7. Feasibility Study • Goal: demonstrate that accurate predictive models can be built • Using step-wise quality measurements as input • Predict end-of-line electrical parameters plus final product yield • Uses Neural Network-based predictive engine • Adaptive • Flexible • Accurate

8. Feasibility Study Data Collection • National Semiconductor, South Portland, ME • 0.18μm CMOS technology • Covering 2 different product designs • Data from 381 wafers, 33 lots • Data Collected: • Quality measurements from CMP, Photo, Etch • All metal layers (31 operations in total) • 15 End of Line electrical parameters • Final Yield (% good die)

9. End of Line Parameters Modelled • Metal 1,2,3 Bridging • N-Type Silicide Bridging • P-Type Silicide Bridging • Via 1,2,3 Contact Resistance • Metal 1,2,3,4 Continuity Resistance • Poly Continuity Resistance • Product Yield

10. Measurements Used As Model Inputs • Metal layers • Photoresist top and bottom CD • Post-etch top and bottom CD • Defect density • Dielectric layers • Pre and post CMP thickness and non-uniformity • Pre and post CMP ILD thickness • Via Layers • Photoresist bottom CD (dense and isolated structures) • Post-etch bottom CD (dense and isolated structures)

11. Data Preparation I • Quality metrics measured on a sample basis after each processing step. • Not always on the same wafers within the lot • Results is a sparse data set, insufficient for model training. • Algorithm to supply estimates for the missing measurements: • Use lot-based average if available. • Use time-based moving average otherwise.

12. Data Preparation II • Merge quality metric data from all processing steps with the end-of-line electrical parameters and final yield • Using lot number and wafer identifier • Divide data into two subsets • Training data (70% of total) • Divided into Train and Test • Validation data (remaining 30%) • Never used during Neural Network training • Helps avoid over-fitting

13. Neural Model Training • Use one Neural Network each for: • End of Line electrical parameters • Final Yield • Minimal Network acceptance criteria: • Root Mean Square Error (RMSE) must be less than standard deviation of the observed data • Better than “guessing the mean” • Model must generalize: • RMSE (validation data) < (1+α) RMSE (training data) • Acceptable accuracy measure.

14. NeuMath Accuracy Measure • Each EOL parameter assigned a target and limits in accordance with product specification. • Divide limits range into 7 regions • Accuracy defined as the fraction of time the observed and predicted fall into the same sub-region. • Since accuracy is tied to the spec limits: • Remains consistent with current decision-making criteria • The fraction of time the decision would be the same using the prediction as it would be using the observed value.

15. Results Overview • Models for 14 of the 16 End Of Line measurements converged : • Including final yield. • Provide an average accuracy of 90%!! • Comparison of RMS Errors of validation and training sets shows very good model generalization. • Critical for model-based decision-making.

16. Predictive Model PerformanceMetal 1 Bridging

17. Prediction Performance for Metal 1 Continuity Resistance

18. Prediction Performance for Via 1 Contact Resistance

19. Prediction Performance for Poly Continuity Resistance

20. Prediction Performance for N-Type Silicide Bridging

21. Prediction Performance for P-Type Silicide Bridging

22. Predictive Model Performance for Product Yield

23. Overall Yield

24. Optimization Results Summary

25. Conclusions / Next Steps • Feasibility study a success • Excellent Model performance (90% accuracy) • Optimization shows possible EOL improvements of up to 22% • Next Steps • Repeat on additional data sets from our partner and additional partners • Full beta trial early 2005 • Software product release Q2 2005