Diagnostic methods for the classifiers and the defects captured by optical tools

Patent metadata

Field	Value
Publication number	US-11237119-B2
Application number	US-201715835399-A
Country	US
Kind code	B2
Filing date	Dec 7, 2017
Priority date	Jan 10, 2017
Publication date	Feb 1, 2022
Grant date	Feb 1, 2022

Abstract

Wafer inspection with stable nuisance rates and defect of interest capture rates are disclosed. This technique can be used for discovery of newly appearing defects that occur during the manufacturing process. Based on a first wafer, defects of interest are identified based on the classified filtered inspection results. For each remaining wafer, the defect classifier is updated and defects of interest in the next wafer are identified based on the classified filtered inspection results.

First claim

What is claimed is:

1. A system for detecting defects of interest in a plurality of wafers comprising:
a central storage media configured to store a plurality of classified inspection results and an initial defect classifier;
a wafer inspection tool;
an image data acquisition system; and
a processor in electronic communication with the central storage media, the wafer inspection tool, and the image data acquisition system, the processor configured to execute the instructions of:
an inspection engine which instructs the processor to:
receive inspection results of a first wafer from the wafer inspection tool;

a sampling engine which instructs the processor to:
retrieve the initial defect classifier from the central storage media;
filter the inspection results based on the initial defect classifier;
review locations of interest on the first wafer from the image data acquisition system, based on the filtered inspection results;
classify the filtered inspection results based on the initial defect classifier;
store the classified filtered inspection results in the central storage media; and
identify defects of interest in the first wafer based on the classified filtered inspection results, wherein the step of identifying defects of interest comprises:
sampling on both sides of a classification boundary between the defects of interest and nuisance of a most recent defect classifier;
obtaining information about classifier stability based on classification fluctuations in the most recent defect classifier;
observing a movement in the classification boundary using the most recent defect classifier; and
identifying the defects of interest based on the predicted movement in the classification boundary, wherein the predicted movement is a probable direction of the movement in the classification boundary;

a tuning engine which instructs the processor to:
update the initial defect classifier based on the stored classified filtered inspection results in the central storage media;

wherein for each remaining wafer:
the inspection engine instructs the processor to:
receive inspection results of a next wafer from the wafer inspection tool;

the sampling engine instructs the processor to:
filter the inspection results of the next wafer based on the initial defect classifier;
review locations of interest on the next wafer, using the image data acquisition system based on the filtered inspection results of the next wafer and historical analysis sampling;
classify the filtered inspection results of the next wafer based on the reviewed locations of interest on the next wafer;
store the classified filtered inspection results for the next wafer in the central storage media;
update the defect classifier, using the processor, based on the stored classified filtered inspection results for the next wafer in the central storage media; and
identify defects of interest in the next wafer based on the classified filtered inspection results for the next wafer.

2. The system of claim 1 , wherein, for each of the remaining wafers:
the tuning engine instructs the processor to:
update the defect classifier, using the processor, based on the stored classified filtered inspection results for the next wafer in the central storage media;

wherein the sampling engine instructs the processor to perform the filtering step based on the updated defect classifier.

3. The system of claim 1 , wherein the image data acquisition system is a scanning electron microscope review tool.

4. The system of claim 1 , wherein the wafer inspection tool performs a hot scan to capture inspection results.

5. The system of claim 1 , wherein the defect classifier sends defect of interest data and nuisance data for retraining of the defect classifier.

6. The system of claim 1 , further comprising storing the inspection results or reviewed locations of interest in the central storage media.

7. The system of claim 1 , wherein the wafer inspection tool is a broadband plasma inspection tool.

8. A method for identifying defects of interest in a plurality of wafers comprising:
receiving, at a processor, inspection results of a first wafer from a wafer inspection tool;
filtering, using the processor, the inspection results based on an initial defect classifier;
reviewing locations of interest on the first wafer, using an image data acquisition system, based on the filtered inspection results;
classifying the filtered inspection results, using the processor, based on the reviewed locations of interest on the first wafer;
storing the classified filtered inspection results in a central storage media;
identifying defects of interest in the first wafer based on the classified filtered inspection results, wherein the step of identifying defects of interest comprises:
sampling on both sides of a classification boundary between the defects of interest and nuisance of a most recent defect classifier;
obtaining information about classifier stability based on classification fluctuations in the most recent defect classifier;
observing a movement in the classification boundary using the most recent defect classifier; and
identifying the defects of interest based on the predicted movement in the classification boundary, wherein the predicted movement is a probable direction of the movement in the classification boundary; and

for each remaining wafer:
receiving, at the processor, inspection results of a next wafer from the wafer inspection tool;
filtering, using the processor, the inspection results based on the initial defect classifier;
reviewing locations of interest on the next wafer, using the image data acquisition system, based on the filtered inspection results of the next wafer and historical analysis sampling;
classifying the filtered inspection results of the next wafer, using the processor, based on the reviewed locations of interest on the next wafer;
storing the classified filtered inspection results for the next wafer in the central storage media;
updating the defect classifier, using the processor, based on the stored classified filtered inspection results for the next wafer in the central storage media; and
identifying defects of interest in the next wafer based on the classified filtered inspection results for the next wafer.

9. The method of claim 8 , wherein the image data acquisition system is a scanning electron microscope review tool.

10. The method of claim 8 , wherein the wafer inspection tool performs a hot scan to capture inspection results.

11. The method of claim 8 , wherein the defect classifier sends defect of interest data and nuisance data for retraining of the defect classifier.

12. The method of claim 8 , further comprising, for each of the remaining wafers:
updating the defect classifier, using the processor, based on the stored classified filtered inspection results for the next wafer in the central storage media;
wherein the filtering step is performed based on the updated defect classifier.

13. The method of claim 8 , further comprising storing the inspection results or reviewed locations of interest in the central storage media.

14. The method of claim 8 , wherein the wafer inspection tool is a broadband plasma inspection tool.

15. The method of claim 8 , wherein the step of updating the defect classifier based on the stored classified filtered inspection results in the central storage media comprises:
estimating a cap rate based on a calculated training confusion matrix, wherein the calculated training confusion matrix is based on the stored classified filtered inspection results for

Assignees

• Kla Tencor Corp

Inventors

• Plihal Martin
• Soltanmohammadi Erfan
• Paramasivam Saravanan
• Ravu Sairam
• Jain Ankit
• Uppaluri Prasanti
• Ramachandran Vijay

Classifications

• H10P74/23

  characterised by multiple measurements, corrections, marking or sorting processes · CPC title

• H10P72/0616

  Monitoring of warpages, curvatures, damages, defects or the like · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G05B2219/32196

  Store audit, history of inspection, control and workpiece data into database · CPC title

• Y02P90/02

  Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS] · CPC title