Yield prediction and statistical process control using predicted defect related yield loss

• US 6,496,958 B1
• Filed: 04/27/1999
• Issued: 12/17/2002
• Est. Priority Date: 04/27/1999
• Status: Expired due to Term

• Current Assignee: Polaris Innovations Limited (Wi-LAN Inc.)
• Original Assignee: Infineon Technologies Richmond LP (Infineon Technologies AG)
• Inventors: Ott, Reinhold, Ollendorf, Heinrich, Lammering, Herbert
• Primary Examiner(s): Smith, Matthew
• Assistant Examiner(s): GARBOWSKI, LEIGH M
• Application Number:
  US09/299,979
• Time in Patent Office: 1,330 Days
• Field of Search: 716/4, 716/19, 716/20, 716/21, 716/1, 716/2, 700/108, 700/109, 700/110, 700/121
• US Class Current: 700/110
• CPC Class Codes: G01N 21/93 Detection standards; Calibrating baseline adjustment, drift correction
• CPC Class Codes: G01N 21/9501 Semiconductor wafers manufacturing processes per se of semiconductor devices implementing a measuring step H01L22/10
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: H01L 22/26 Acting in response to an ongoing measurement without interruption of processing, e.g. endpoint detection, in-situ thickness measurement endpoint detection arrangements in CMP apparatus B24B37/013, in discharge apparatus H01J37/32

Abstract

In accordance with the present invention, a method, which may be implemented by employing a program storage device, for determining yield loss for a device includes the steps of determining killing probabilities corresponding to values of inspection parameters based on historic inspection information, determining defects on the device and ordering the defects by classifying the defects according to the inspection parameters. The defects adopt the killing probabilities associated with the same values of the inspection parameters. The method further includes the step of calculating a predicted yield loss based on the defects and the adopted killing probabilities. The method further includes the step of applying statistical process control to the predicted yield loss for all in-line inspection (process) steps.

Claims

1. A method for determining yield loss for a device comprising the steps of:

determining killing probabilities corresponding to values of inspection parameters based on historic inspection information;

determining defects on the device;

classifying the defects according to the inspection parameters, the defects adopting the killing probabilities associated with the same values of the inspection parameters; and

calculating a predicted yield loss for each of a plurality of inspection processes based on the defects and the adopted killing probabilities.

2. The method as recited in claim 1, wherein the step of determining defects on the device includes the step of inspecting the device using inspection tools.

3. The method as recited in claim 1, wherein the step of classifying the defects includes the steps of:

determining defect inspection parameters used to determine each defect;

finding corresponding values of the defect inspection parameters with the values of the inspection parameters determined based on historic information; and

associating the killing probability of the values of the inspection parameters determined based on historic information with each defect having the corresponding values.

4. The method as recited in claim 1 wherein the step of calculating a predicted yield loss for each of a plurality of inspection processes is performed by calculating the predicted yield loss according to the equation:

$\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y_k=1-\underset{\mathrm{ij}}{\prod <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(1-{\mathrm{kp}}_{\mathrm{kij}}<br><br>n_{\mathrm{kij}}\right)$

where Δ

Y_k is the predicted yield loss for a process k, kp_k is the adopted killing probability for a defect n_k and i and j are counters.

5. The method as recited in claim 1, further comprising the step of calculating an overall predicted yield loss based on the predicted yield lose of the plurality of inspection processes.

6. The method as recited in claim 5, wherein the step of calculating an overall predicted yield loss based on the predicted yield loss of the plurality of inspection processes is performed by calculating the overall predicted yield loss according to the equation:

$\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y=1-\underset{k}{\prod <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(1-\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y_k\right),$

where Δ

Y_k is the predicted yield loss for a process k and Δ

Y is the overall predicted yield loss.

7. The method as recited in claim 1, further comprising the step of graphically representing the predicted yield loss.

8. The method as recited in claim 1, wherein the step of classifying the defects is performed automatically by a machine.

9. The method as recited in claim 1, further comprising the step of applying statistical process control to the predicted yield loss.

10. A method as defined in claim 1 wherein said classifying is performed automatically.

11. A method for determining yield loss for semiconductor wafers comprising the steps of:

creating a parametric space defined by values of at least two inspection parameters;

assigning killing probabilities corresponding to values of the at least two inspection parameters based on historic inspection information;

inspecting the semiconductor device to determine defects on the semiconductor device;

ordering the defects on the semiconductor device by classifying the defects according to the at least two inspection parameters, the classified defects adopting th killing probabilities of the parametric space to which the defects are assigned; and

calculating a predicted yield loss for each of a plurality of inspection processes based on the defects and the adopted killing probabilities.

12. The method as recited in claim 11, wherein the step of creating a parametric space defined by values of at least two inspection parameters further comprises the steps of:

forming an m-dimensional space by providing m inspection parameters, the m-dimensional space including subspaces; and

assigning values of each of the m inspection parameters to each subspace such that each subspace represents one of a given value and a range of values for each of the m inspection parameters.

13. The method as recited in claim 11, wherein the step of inspecting the semiconductor device to determine defects on the semiconductor device includes the steps of:

inspecting the semiconductor device using inspection tools; and

generating a results file, the results file including defect size and location information and other inspection parameter information.

14. The method as recited in claim 11, wherein the step of ordering the defects includes the steps of:

determining defect inspection parameters used to determine each defect;

finding corresponding values of the defect inspection parameters with the values of the at least two inspection parameters determined based on historic information; and

associating the killing probability of the values of the at least two inspection parameters determined based on historic information with each defect having the corresponding values.

15. The method as recited in claim 11 wherein the step of calculating a predicted yield loss for each of a plurality of inspection processes is performed by calculating the predicted yield loss according to the equation:

$\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y_k=1-\underset{\mathrm{ij}}{\prod <br><br>}<br><br>\left(1-{\mathrm{kp}}_{\mathrm{kij}}<br><br>n_{\mathrm{kij}}\right)$

where Δ

Y_k is the predicted yield loss for a process k, kp_k is the adopted killing probability for a defect n_k and i and j are counters.

16. The method as recited in claim 11, further comprising the step of calculating an overall predicted yield loss based on the predicted yield loss of the plurality of inspection processes.

17. The method as recited in claim 16, wherein the step of calculating an overall predicted yield loss based on the predicted yield loss of the plurality of inspection processes is performed by calculating the overall predicted yield loss according to the equation:

$\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y=1-\underset{k}{\prod <br><br>}<br><br>\left(1-\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y_k\right)$

where Δ

Y_k is the predicted yield loss for a process k and Δ

Y is the overall predicted yield loss.

18. The method as recited in claim 11, further comprising the step of graphically representing the predicted yield loss.

19. The method as recited in claim 11, wherein the step of ordering the defects is performed automatically by a machine.

20. The method as recited in claim 11, further comprising the step of applying statistical process control to the predicted yield loss.

21. A method as defined in claim 11 wherein said ordering by classifying is performed automatically.

22. A method for determining yield loss for semiconductor wafers comprising the steps of:

computing killing probabilities based on historic data for subspaces in a parameter space, the parameter space being divided in to subspaces by values of inspection parameters;

inspecting the semiconductor wafer to generate defect information in accordance with predetermined inspection parameters;

ordering defects by determining defect inspection parameters used to determine each defect and finding corresponding subspaces to assign the defects;

associating the killing probability of each subspace to the defects assigned to the subspace;

determining a predicted yield loss for each of a plurality of inspection processes based on a number of defects in each subspace and the killing probability assigned to each subspace;

applying statistical process control to the predicted yield loss; and

determining an overall predicted yield loss for the semiconductor wafer based on the predicted yield loss for each of the plurality of inspection processes.

23. The method as recited in claim 22, wherein the step of determining a predicted yield loss for each of a plurality of inspection processes is performed by calculating the predicted yield loss according to the equation:

$\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y_k=1-\underset{\mathrm{ij}}{\prod <br><br>}<br><br>\left(1-{\mathrm{kp}}_{\mathrm{kij}}<br><br>n_{\mathrm{kij}}\right)$

where Δ

Y_k is the predicted yield loss for a process k, kp_k is the killing probability for a defect n_k in a subspace ij, where i and j represent counters for a two dimensional parameter space.

24. The method as recited in claim 22, wherein the step of calculating an overall predicted yield loss is performed by calculating the overall predicted yield loss according to the equation:

$\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y=1-\underset{k}{\prod <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(1-\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y_k\right)$

where Δ

Y_k is the predicted yield loss for a process k and Δ

Y is the overall predicted yield loss.

25. The method as recited in claim 22, further comprising the step of graphically representing the predicted yield loss and the overall predicted yield loss.

26. The method as recited in claim 22, wherein the step of ordering the defects is performed automatically by a machine.

27. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for determining yield loss for a device, the method steps comprising;

determining killing probabilities corresponding to values of inspection parameters based on historic inspection information;

determining defects on the device;

classifying the defects according to the inspection parameters, the defects adopting the killing probabilities associated with the same values of the inspection parameters; and

calculating a predicted yield loss for each of a plurality of inspection processes based on the defects and the adopted killing probabilities.

28. The program storage device as recited in claim 27, wherein the step of classifying the defects includes the steps of:

determining defect inspection parameters used to determine each defect;

finding corresponding values of the defect inspection parameters with the values of the inspection parameters determined based on historic information; and

associating the killing probability of the values of the inspection parameters determined based on historic information with each defect having the corresponding values.

29. The program storage device as recited in claim 27 wherein the step of calculating a predicted yield loss for each of a plurality of inspection processes is performed by calculating the predicted yield loss according to the equation:

$\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y_k=1-\underset{\mathrm{ij}}{\prod <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(1-{\mathrm{kp}}_{\mathrm{kij}}<br><br>n_{\mathrm{kij}}\right)$

where Δ

Y_k is the predicted yield loss for a process k, kp_k is the adopted killing probability for a defect n_k and i and j are counters.

30. The program storage device as recited in claim 27, further comprising the step of calculating an overall predicted yield loss based on the predicted yield loss of the plurality of inspection processes.

31. The program storage device as recited in claim 30, wherein the step of calculating an overall predicted yield loss based on the yield loss of the plurality of inspection processes is performed by calculating the overall predicted yield loss according to the equation:

$\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y=1-\underset{k}{\prod <br><br>}<br><br>\left(1-\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>Y_k\right)$

where Δ

Y_k is the predicted yield loss for a process k and Δ

Y is the overall predicted yield loss.

32. The program storage device as recited in claim 27, wherein the step of classifying the defects is performed automatically by a machine.

33. The program storage device as recited in claim 27, further comprising the step of applying statistical process control to the predicted yield loss.

34. A program storage device as defined in claim 27 wherein said classifying is performed automatically.