Dynamic inline yield analysis and prediction

• US 20080294281A1
• Filed: 05/22/2008
• Published: 11/27/2008
• Est. Priority Date: 05/24/2007
• Status: Active Grant

• Current Assignee: Applied Materials Incorporated
• Original Assignee: Applied Materials Incorporated
• Inventors: Schwarm, Alexander T., Shimshi, Rinat, Svidenko, Vicky, Jawaharlal, Sundar, Nehmadi, Youval
• Granted Patent: US 7,937,179 B2
• Time in Patent Office: Days
• US Class Current: 700/110
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/32222 Fault, defect detection of origin of fault, defect of product
• CPC Class Codes: G06F 2111/08 Probabilistic or stochastic CAD
• CPC Class Codes: G06F 30/3323 using formal methods, e.g. equivalence checking or property checking
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 50/04 Manufacturing
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• CPC Class Codes: Y02P 90/30 Computing systems specially adapted for manufacturing

Abstract

In one embodiment, a method for predicting yield includes calculating a criticality factor (CF) for each of a plurality of defects detected in an inspection process step of a wafer, and determining a yield-loss contribution of the inspection process step to the final yield based on CFs of the plurality of defects and the yield model built for a relevant design. The yield-loss contribution of the inspection process step is then used to predict the final yield for the wafer.

Claims

1. A computer-implemented method comprising:

calculating a criticality factor (CF) for each of a plurality of-defects detected in an inspection process step of a wafer;

determining a yield-loss contribution of the inspection process step to a final yield based on CFs of the plurality of defects and a yield model built for a relevant design; and

predicting the final yield for the wafer using the yield-loss contribution of the inspection process step.

2. The computer-implemented method of claim 1, further comprising determining an adjusted number of wafer starts based on the predicted final yield.

3. The computer-implemented method of claim 1, further comprising building the yield model for the relevant design by:

distributing defects among a number of inspection steps; and

for each step;

classifying the defects according to similar CF values into one or more CF bins;

creating a subset from a learning set, wherein the learning set is a set of dies having inspection results in all inspection steps, wherein the subset is a set of dies having defects only in a specific inspection step;

binning together the dies in the subset according to a similar CF value to create one or more sub-subsets;

for each sub-subset, calculating a defect-specific fail probability (FP_defects^s,b); and

plotting the defect-specific fail probability in a functional form, FP_defects=f(CF), to create a correlation curve.

4. The computer-implemented method of claim 3, wherein calculating the defect-specific fail probability (FP_defects^s,b) for each sub-subset is calculated as:

$F<br><br><br><br>P_{\mathrm{defects}}^{s,b}=1-\frac{1}{c}·<br><br>Y_{\mathrm{DirtyDies}}^{\mathrm{SubsetC}}=1-\frac{1}{c}·<br><br>\frac{\mathrm{GoodDiesInSubsetC}}{\mathrm{AllDiesInSubsetC}}$

5. The computer-implemented method of claim 4, wherein c is a constant value representing a non-defect yield component for a subset of the learning set of dies having no visual defects calculated as a ratio of passing dies having no visual defects to all dies having no visual defects.

6. The computer-implemented method of claim 1, wherein calculating the CF comprises:

identifying an open area, wherein the open area is a total area of all locations inside a design layout area to cause an open failure, and identifying a short area, wherein the short area is a total area of all locations inside the design layout to cause a short failure; and

calculating a probability of the defect to cause an open failure and a probability of the defect to cause a short failure.

7. The computer-implemented method of claim 6, wherein the probability of the open failure is a ratio between the open area and a design layout window defined by a location inaccuracy of an inspection tool.

8. The computer-implemented method of claim 6, wherein the probability of the short failure is a ratio between the short area and the entire design layout area.

9. The computer-implemented method of claim 1, wherein determining the yield-loss contribution of the process step to the final yield comprises:

for each defect on an inspected layer, extracting the defect-specific fail probability value (FP_defects) from the correlation curve corresponding to the step; and

computing the yield-loss contribution to final yield as;

$Y<br><br><br><br>L_{\mathrm{defects}}^s=1-\underset{b=1}{\overset{B_s}{\prod <br><br>}}<br><br><br><br>{\left(1-F<br><br><br><br>P_{\mathrm{defects}}^{s,b}\times <br><br>\frac{N_{s,b}}{N}\right)}^{N_b}$

10. The computer-implemented method of claim 1, wherein predicting the final yield for a wafer that has completed all inspection process steps comprises:

using the defect-specific fail probability value (FP_defects) from the correlation curve and a defective die count per CF bin to compute the final yield as;

$Y_{\mathrm{final}}=c·<br><br>\underset{s=1}{\overset{S}{\prod <br><br>}}<br><br><br><br>\underset{b=1}{\overset{B_s}{\prod <br><br>}}<br><br><br><br>{\left(1-F<br><br><br><br>P_{\mathrm{defects}}^{s,b}\times <br><br>\frac{N_{s,b}}{N}\right)}^{N_b}$

11. The computer-implemented method of claim 1, wherein predicting the final yield for an inline wafer comprises:

using the defect-specific fail probability value (FP_defects) from the correlation curve and a defective die count per CF bin to compute the final yield as;

$Y_{\mathrm{final}}=c·<br><br>\underset{s=1}{\overset{M}{\prod <br><br>}}<br><br><br><br>\underset{b=1}{\overset{B_s}{\prod <br><br>}}<br><br><br><br>{\left(1-F<br><br><br><br>P_{\mathrm{defects}}^{s,b}\times <br><br>\frac{N_{s,b}}{N}\right)}^{N_b}·<br><br>\underset{s=M+1}{\overset{S}{\prod <br><br>}}<br><br>\left(1-〈<br><br>Y<br><br><br><br>L_{\mathrm{defects}}^s〉<br><br>\right)$

where YL_defects^s is an average yield-loss contribution over a historical time span.

12. The computer-implemented method of claim 1, wherein there is more than one defect per die, further comprising:

aggregating all of the defects in a die into a virtual defect; and

for each virtual defect, calculating an aggregated CF value as;

${\mathrm{CF}}_{\mathrm{die}}=1-\underset{i=1}{\overset{\mathrm{DefectsPerDie}}{\prod <br><br>}}<br><br><br><br>\left(1-{\mathrm{CF}}_i\right)$

13. A computer-implemented system comprising:

a model constructor, to build a yield model for a design; and

a yield analyzer, coupled to the model constructor, to predict a yield based on the built yield model and criticality factors (CFs) calculated for individual defects detected at different process steps.

14. The computer-implemented system of claim 13, wherein the yield analyzer comprises:

a CF calculator, to calculate the CFs of the individual defects; and

a yield predictor, to calculate a yield-loss contribution of an inspection process step to a final yield using the CFs.

15. The computer-implemented system of claim 14, wherein the yield-loss predictor to calculate the yield contribution of the inspection process step to the final yield comprises correlating the CFs calculated for the defects in a specific process step to an observed die yield provided by the yield model.

16. The computer-implemented system of claim 13, further comprising:

a yield model database, coupled to the model constructor, to store the yield model;

a design data database, coupled to the yield analyzer, to store design data, wherein the design data includes at least one of design layouts and routing information for the design layouts; and

a defect reporter, coupled to the yield analyzer, to provide geometric characteristics of individual defects detected during different process steps.

17. The computer-implemented system of claim 13, wherein the CFs are calculated based on at least one of:

a size of the defect, a volume of the defect, and the layout of the design on which the defect is found.

18. The computer-implemented system of claim 13, wherein to build a yield model for a design uses sort yield data and a learning set comprising a set of dies having inspection results for all critical process steps.

19. A computer-implemented apparatus comprising:

means for calculating a criticality factor (CF) for each of a plurality of defects detected in an inspection process step of a wafer;

means for determining a yield-loss contribution of the inspection process step to a final yield based on CFs of the plurality of defects and a yield model built for a relevant design; and

means for predicting the final yield for the wafer using the yield-loss contribution of the inspection process step.

20. A computer readable storage medium, comprising executable instructions which when executed on a processing system cause the processing system to perform a method comprising:

calculating a criticality factor (CF) for each of a plurality of defects detected in an inspection process step of a wafer;

determining a yield-loss contribution of the inspection process step to a final yield based on CFs of the plurality of defects and a yield model built for a relevant design; and

predicting the final yield for the wafer using the yield-loss contribution of the inspection process step.