Kla Tencor Corporation

Christopher W Lee

Robinson Piramuthu

Martin Plihal

Patrick Huet

Yan Xiong

Cho Huak Teh

Francis Raquel

Matthew Manzer

The present disclosure is directed to a method of tool matching that employs an auto-learning feedback loop to update a library of key parameters. According to the method, measurements are performed on a control wafer to collect a set of parameters associated with the process/analysis tool that is being matched. When deviated parameters correlate to a correctable tool condition (i.e. a tool matching event), the parameters are added to the library of key parameters. These key or critical parameters may be monitored on a more frequent basis to identify deviations that have a strong likelihood of matching with a correctable tool condition. The tool matching methodology advantageously allows for monitoring of an automatically updated list of key parameters instead of needing to look at the full set of parameters collected from a control wafer each time. As such, tool matching can be performed on a more frequent basis.

Method for auto-learning tool matching

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.

Process excursion detection

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San Ramon, CA, United States of America

Christopher W Lee