Process Excursion Detection

• US 20100067781A1
• Filed: 11/25/2009
• Published: 03/18/2010
• Est. Priority Date: 11/19/2003
• Status: Abandoned Application

• Current Assignee: KLA-Tencor Corporation
• Original Assignee: KLA-Tencor Corporation
• Inventors: Lee, Christopher W., Piramuthu, Robinson, Xiong, Yan, Huet, Patrick Y., Teh, Cho H., Plihal, Martin
• Application Number:
  US12/626,021
• Publication Number:
  US 20100067781A1
• Time in Patent Office: Days
• US Class Current: 382/149
• CPC Class Codes: G01N 2021/8854 Grading and classifying of flaws
• CPC Class Codes: G01N 2021/8864 Mapping zones of defects
• CPC Class Codes: G01N 21/8851 Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges G01N21/8806 and G01N21/93 - G01N21/95692 take precedence; optical measurement of dimensions G01B11/00; optical scanning G02B26/10; image transformation G06T3/00; computerised image enhancement G06T5/00; image processing per se for flaw detection G06T7/0002
• 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

Abstract

A method for analyzing defect information on a substrate, including logically dividing the substrate into zones, and detecting defects on the substrate to produce the defect information. The defect information from the substrate is analyzed on a zone by zone basis to produce defect level classifications for the defects within each zone. The zonal defect level classifications are analyzed according to at least one analysis method. The defect level classifications are preferably selected from a group of defect level classifications that is specified by a recipe. Preferably, the at least one analysis method includes at least one of zonal defect distribution, automatic defect classification, spatial signature analysis, and excursion detection. The defect level classifications preferably include at least one of individual defect, defect cluster, and spatial signature analysis signature. In one embodiment the defect information is logically divided into configurable zones after the defects on the substrate have been detected.

Claims

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11. A method for detecting process excursions, the method comprising the steps of:

detecting defects on a selectable set of substrates,

compositing the defects detected on the set of substrates into an effectual substrate defect set, and

analyzing the effectual substrate defect set with a spatial analysis routine.

12. The method of claim 11, wherein the spatial analysis routine includes at least one of spatial signature analysis and repeater analysis.

13. The method of claim 11, wherein the selectable set of substrates includes substrates that all belong to a given lot.

14. The method of claim 11, wherein the selectable set of substrates includes every n^th substrate from a given data set, where n is an integer that is greater than one.

15. The method of claim 11, wherein the step of compositing the defects into an effectual substrate defect set includes at least one of translating and rotating a data set from a given substrate as needed to align with data sets from other substrates.

16. A method for classifying defects on a substrate, the method comprising the steps of:

analyzing the defects with a first analysis routine that is adapted to classify larger patterns of defects, and

analyzing the defects that were not classified with the first analysis routine with a second analysis routine that is adapted to classify smaller defects, using output from the first analysis routine as input to the second analysis routine.

17. The method of claim 16, wherein the first analysis routine is spatial signature analysis.

18. The method of claim 16, wherein the second analysis routine is automated defect classification.

19. The method of claim 16, wherein the output from the first analysis routine includes bounding boxes from a spatial signature analysis.

20. The method of claim 16, wherein the first analysis routine is spatial signature analysis and the second analysis routine is automated defect classification, and the output from the first analysis routine includes bounding boxes.