Semiconductor wafer examination system

• US 20030164942A1
• Filed: 12/07/2000
• Published: 09/04/2003
• Est. Priority Date: 12/07/1999
• Status: Active Grant

• Current Assignee: Sony Corporation (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.)
• Inventors: Take, Kunihiko
• Granted Patent: US 7,035,447 B2
• Time in Patent Office: Days
• US Class Current: 356/237.2
• CPC Class Codes: G01N 2021/8854 Grading and classifying of flaws
• CPC Class Codes: G01N 2021/8896 Circuits specially adapted for system specific signal conditioning
• CPC Class Codes: G01N 21/9501 Semiconductor wafers manufacturing processes per se of semiconductor devices implementing a measuring step H01L22/10
• CPC Class Codes: G01N 21/95607 using a comparative method

Abstract

A semiconductor wafer examination system can accurately and reliably detects defects of semiconductor wafers. The semiconductor wafer examination system 1 comprises a defect classification device for automatically classifying defects of semiconductor wafers on the basis of defect detection parameters and a knowledge base and a classification support device for supporting the operation of the defect classification device. The defect detection parameters define the permissible deviation of the surface image of a defective semiconductor wafer from that of a normal semiconductor wafer. The knowledge base contains data for the types of defects that can occur in semiconductor wafers and data for showing the characteristics of each type. The classification support device prepares data on isolated defective areas that are used for selecting and/or altering the defect detection parameters and preparing the knowledge base.

Claims

1. A semiconductor wafer examination system comprising:

a defect classification device adapted to pick up an image of the surface (defect image) of a defective semiconductor wafer, compare the defect image with an image of the surface of a normal semiconductor wafer (normal image), identify each defective area isolated as characteristic area of a defect in the defect image on the basis of the outcome of the comparison and defect detection parameters for defining threshold values for defects and automatically determine the type of defect of the defective area on the basis of a knowledge base for determining the type of defect according to the characteristic quantity of the defective area; and

a classification support device including a classification means for identifying the defective areas of a plurality of defect images on the basis of the normal image and the defect detection parameters and classifying the identified defective areas;

a defective area display means for displaying the plurality of defective areas as classified by said classification means, an editing means for editing the defect detection parameters on the basis of the defective areas displayed by said defective area display means, a classification result re-instructing means for manually re-classifying the result of classification of the defective areas obtained by said classification means and a selection means for selecting classified defect image data for preparing the knowledge base from the plurality of defective areas as classified by the classification result re-instructing means.