FAB yield enhancement system

• US 6,408,219 B2
• Filed: 05/11/1998
• Issued: 06/18/2002
• Est. Priority Date: 05/11/1998
• Status: Expired due to Term

• Current Assignee: Applied Materials Incorporated
• Original Assignee: Applied Materials Incorporated
• Inventors: Lamey, Patrick H Jr., Yaron, Gad, Maimon, Amotz
• Primary Examiner(s): Wiley, David
• Assistant Examiner(s): Backer, Firmin
• Application Number:
  US09/075,254
• Publication Number:
  US 20010051836A1
• Time in Patent Office: 1,499 Days
• Field of Search: 700/110, 438/14, 438/16, 438/17, 438/18, 364/552, 364/468.7, 382/149, 702/83, 148/33, 365/552, 710/110, 710/121, 710/103
• US Class Current: 700/110
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps

Abstract

A yield enhancement system organizes defect classification and attribute information into a global classification scheme. The global classes are used to identify defect sources and to generate inspection and review plans. The system accumulates defect information in a database and continually refines the information to improve the accuracy of the classification assignments and the identification of the defect sources.

Claims

1. A defect classification method, for use in a semiconductor fabrication plant, said method comprising:

inspecting a wafer using a plurality of wafer analysis tools;

providing local classification based on defect data from each of the plurality of wafer analysis tools;

integrating said local classifications; and

assigning a global classification for the defect based on the integration.

2. The method of claim 1 wherein the assigning step further comprises the step of comparing at least one attribute of the global classification with yield data from the fabrication plant.

3. The method of claim 1 wherein the assigning step further comprises the step of comparing the defect attribute data with attribute data stored in database.

4.The method of claim 1 further comprising the step of confidence level assignment, wherein a confidence level is assigned to the global classification based on empirical defect data.

4. The method of claim 1 wherein the assigning step further comprises the step of comparing an attribute of the global classification with spatial signature analysis data associated with the defect.

5. The method of claim 1 further comprising the step of identifying, based on the global classification, at least one suggested source of the defect.

6. The method of claim 1 further comprising:

generating update data for at least one of a wafer inspection plan and a wafer review plan wherein said update data is used to validate algorithms in an ongoing basis and refine the wafer inspection and wafer review plan.

7. The method of claim 1 further comprising the step of generating corrective action data for a wafer process tool.

8. The method of claim 1 further comprising the step of initiating a verification operation for a wafer process tool.

9. An automatic yield enhancement system for a semiconductor plant comprising:

a plurality of wafer analysis tools, including inspection tools and review tools, adapted to provide local classification data for a defect, wherein each of said tools provide a separated local classification data;

a global automatic defect classification module adapted to receive the local classification data and provide a global classification data;

a defect reference database adapted to store characteristics of wafer defects; and

a defect source identification module adapted to receive the global defect classification and defect data and further adapted to determine defect identification and defect source data.

10. The automatic yield enhancement system of claim 10, further comprising a learning module receiving the global defect classification data and based thereupon adjusting operation parameters of the global automatic defect classification module.

11. The automatic yield enhancement system of claim 10, further comprising a learning module receiving the global defect classification data and sending update information to at least one of the inspection and review tools.

12. The automatic yield enhancement system of claim 10, further comprising a corrective action module receiving data from at least one of the global automatic defect classification module, the defect reference data base, and the defect source identification module, and providing a check list of proposed investigate and corrective actions to be taken for reducing identified defects.

13. The automatic yield enhancement system of claim 10, further comprising an equipment operation parameters module receiving the defect source data and based thereupon sending a self diagnostic instruction to the fabrication plant'"'"'s processing equipment.

14. The automatic yield enhancement system of claim 10, further comprising a sampling module receiving data from the inspection tool and the defect reference database and, based thereupon, generating a defect review sampling plan.

15. The automatic yield enhancement system of claim 10 further receiving defect attributes and classification data from inspection and review tools, and performing the following operations:

for each defect examining whether the classification of the inspection and review tools are the same and, if so, issuing the classification as the global classification;

when the classification of the inspection and review tools is different, examining the defect attributes data to determine global classification for the defect;

when the attributes and classification data do not lead to a predefined certainty in defect classification, searching a defect database to determine a global classification for the defect;

when necessary, based on the global classification, sending a signal to one of the inspection and review tool to adjust classification parameters.

16. A method of identifying a source of a wafer defect in a semiconductor fabrication plant, the method comprising the steps of:

receiving defect classification data from a plurality of wafer analysis tools;

generating a local defect classification from said defect classification data;

generating global defect classification based on integrating said local defect classification;

storing characteristics of various wafer defects in a database; and

determining defect identification and defect source data from the global defect classification.