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US 11,237,119 B2Granted
Diagnostic methods for the classifiers and the defects captured by optical tools
AssigneeKLA-Tencor Corporation
InventorsMartin Plihal, Erfan Soltanmohammadi, Saravanan Paramasivam, Sairam Ravu, Ankit Jain, Prasanti Uppaluri, Vijay Ramachandran
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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.
Claims17
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.
30 more elements
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.
18 more elements
Dependencies are inferred from the claim text.