Virtual Metrology to Measure Mass Loss at Deep Trench Processes

1. Virtual Metrology to Measure Mass Loss at Deep Trench Processes April 18th, 2007 Tilo Wünsche, Matthias Rudolph, Jan Zimpel (adp GmbH)

2. Motivation to Measure the Mass Loss during the Deep Trench Etch Process DT etch hard mask hard mask DmSi Si Si • Deep trench used as storage capacitor • Capacitance is one of main contributors to functionality • Capacitance depends on area of capacitor plate (trench sidewall) • Si mass loss is indicator for sidewall area Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 2

3. Virtual Metrology predicting Mass loss Value for each wafer (high sample rate) Little cycle time consumption Motivation to Measure the Mass Loss during the Deep Trench Etch Process • Mass loss • Correlates with Capacity of storage capacitor • Parameter for short loop control • Measurement of weight before and after etch necessary • not all wafers can be measured, because of time consumption Capacity of DRAM mass loss Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 3

4. Plasma Outline of the PresentationScheme of Data Processing Etch Process / Recording spectra Offline Analysis Data Mining via Ridge Regression / define Areas Preprocessing of OES Data On-line Application of Model Applying Model to APC Trend Building Model via Forward Regression DTml_pred = f(OES_Areas) Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 4

5. Plasma Outline of the PresentationScheme of Data Processing Etch Process / Recording spectra Offline Analysis Data Mining via Ridge Regression / define Areas Preprocessing of OES Data On-line Application of Model Applying Model to APC Trend Building Model via Forward Regression DTml_pred = f(OES_Areas) Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 5

6. Plasma MID: XYZSLOT: 1Recipe: ABC Measure and Processing OES DataSensor Integration OES Sensor Equipment HOST optical fiber FAB LAN recipe start with logistic (MID, Slot, Wafer, Recipe) recipe stop, recipe step OES Data OES Measurement Application OES Analysis Application FDC Application • Spectral data visualization • Data mining (PCA, Modeling) • EP model design • On-line process monitoring • Visualization of process indicators • OCAP SpectralDatabase Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 6

7. Information of the OES Spectrum • Plasma interactions too many gas species and other process parameters • Huge amount of optical emission lines • Complex dependency of emission strength for individual species • Spectral responses characterized on experimental variations of HBr, NF3, Ar, O, SiF4 Break Through Main Etch Response from NF3 Response from SiF4 Response from HBr Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 7

8. Plasma Outline of the PresentationScheme of Data Processing Etch Process / Recording spectra Offline Analysis Data Mining via Ridge Regression / define Areas Preprocessing of OES Data On-line Application of Model Applying Model to APC Trend Building Model via Forward Regression DTml_pred = f(OES_Areas) Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 8

9. Data Mining – Task High dimensional data cube of OES spectra containing information mass loss etch time t wafer 1 ... N wavelength  Objective: Extraction of significant spectral information representing mass loss during etch process Solution:Decomposition of the data cube by unfolding and ridge regression or PCA based methods Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 9

10. Data Mining – Ridge Regression – I • Ridge Regression: • Method to solve an overdetermined system of equations • Favorable with many collinear data sets, e.g. spectral data • creates a model using all predictors • 3-way ridge regression Model allow the extraction of significant spectral ranges and information about important time ranges which carry information about mass loss during etch process Step1 Step2 Step3 426 /440nm wavelength (nm) 519 nm 548 nm 657 nm time /s Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 10

11. Data Mining – Ridge Regression – II wavelength (nm) mass loss time (s) • Whole response pattern could be used as a model predicted mass loss • To predict the mass loss model some significant spectral ranges are sufficient. • Simple automated updated procedure possible • Robust model Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 11

12. Plasma Outline of the PresentationScheme of Data Processing Etch Process / Recording spectra Offline Analysis Data Mining via Ridge Regression / define Areas Preprocessing of OES Data On-line Application of Model Applying Model to APC Trend Building Model via Forward Regression DTml_pred = f(OES_Areas) Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 12

13. Process deviation Long Term Process Effects Chamber pollutes during production • Chamber has to be cleaned, worn parts have to be changed • Production recipe has to be adapted to meet changing conditions mass loss runs Maintenance activities Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 13

14. Preprocessing of OES Data • Normalization to step over wet clean • cycles • Best results by normalization on baseof total intensity of the actual measured spectrum (e.g. integral or norm) HBr Response 1.0 16000 0.8 15000 0.6 14000 0.4 0.2 13000 0.0 12000 -0.2 • Data filtering to exclude • Measurement failures • Bad processes • Only real outliers should be removed • Distribution function of predictors and response have to be kept 11000 -0.4 10000 -0.6 9000 -0.8 8000 -1.0 20 40 60 80 100 120 140 160 Increase of intensity after wet clean Outliers Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 14

15. Plasma Outline of the PresentationScheme of Data Processing Etch Process / Recording spectra Offline Analysis Data Mining via Ridge Regression / define Areas Preprocessing of OES Data On-line Application of Model Applying Model to APC Trend Building Model via Forward Regression DTml_pred = f(OES_Areas) Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 15

16. Forward Regression • Forward Regression: • Method to solve an overdetermined system of equations • Favorable with collinear data sets • Selects to most correlation predictors and skips the others • Creates simple models (little calculation power, easily to implement as equation) • Search for best correlating predictor • p-value • Check if correlation has highly probability • Ypre = A*x + B*x • Add predictor to the model • Err = Ypre - Yact • Apply model and calculate residual error Yes No • Finished Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 16

17. Static Model Model uses: SiF4 response, Si II 518 nm, Si II 545 nm Process deviation mass loss R=0.93 error std(error) = 5.1mg predicted mass loss Run • Model build with all data from three months including all maintenance procedures and process changes • Model works almost perfect • Model can be applied over maintenance activities Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 17

18. Model with Continuously Updating Procedure Process deviation • Continuously adaptation of model • parameters because of • Maintenance activities • Process changes • Prediction model build at every measurement of the actual mass loss including values from the last month mass loss • measured • predicted model parameters • const • SiF4 response • Si II 518 nm • Si II 545 nm runs Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 18

19. Model with Continuously Updating Procedure mass loss error R=0.96 Process deviation std(error)=4.6mg runs predicted mass loss • Significant improvement of prediction quality by adaptive adjustment of Model parameters • The predicted mass loss shows less error at process changes Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 19

20. Plasma Outline of the PresentationScheme of Data Processing Etch Process / Recording spectra Offline Analysis Data Mining via Ridge Regression / define Areas Preprocessing of OES Data On-line Application of Model Applying Model to APC Trend Building Model via Forward Regression DTml_pred = f(OES_Areas) Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 20

21. Connection to APC Trend • Formula predicting the mass loss had to put to APC Trend manually. • To be automated for roll out Value Time axis Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 21

22. Possible Reactions on the Model Output • Actual implemented actions • Model output at APC Trend • Email if mass loss out of spec • Further usage by engineers • Not possible to implement • Real time reaction during wafer processing to stop the process by endpoint detection • Variance of individual values too high, probability to create scrap • Future items to be checked • Centering the process regarding his spec limits • Adapting process steps after the deep trench etching • Could be automated using R2R control Qimonda · Tilo Wünsche · QD P LS PC FDCS · April 18th, 2007 · Page 22

23. Thank you The World’s LeadingCreative Memory Company