Pattern inspecting system and pattern inspecting method

• US 6,407,808 B2
• Filed: 01/19/2001
• Issued: 06/18/2002
• Est. Priority Date: 07/04/1997
• Status: Expired due to Term

• Current Assignee: Hitachi, Ltd.
• Original Assignee: Hitachi, Ltd.
• Inventors: Nozoe, Mari, Yoda, Haruo
• Primary Examiner(s): Pham, Hoa Q.
• Application Number:
  US09/764,450
• Publication Number:
  US 20010009461A1
• Time in Patent Office: 515 Days
• Field of Search: 356/237.1, 356/237.2, 356/237.3, 356/237.4, 356/237.5, 356/394, 250/559.39, 250/559.41, 250/559.45, 382/149, 382/159, 348/126
• US Class Current: 356/237.2
• CPC Class Codes: G01N 21/956 Inspecting patterns on the surface of objects contactless testing of electronic circuits G01R31/308; testing currency G07D; manufacturing processes per se of semiconductor devices implementing a measuring step H01L22/10

Abstract

A visual reinspection of circuit patterns using a reviewing apparatus is omitted from a semiconductor circuit pattern forming process to achieve the minute analysis of detected defects in the circuit patterns quickly. A fast pattern inspecting system comprises a calculating means for calculating the graphical characteristic quantities of the defects in synchronism with the detection of the defects, and a classifying means for classifying the defects in clusters by the calculated characteristic quantities.

Claims

1. A method for defect clustering in inspection patterns on the surface of a workpiece, said method comprising the steps of:

(a) judging whether an occurrence probability distributions of clusters is known or not;

(b) carrying out the following steps (c) and (d), if the occurrence probability distributions of clusters are known, and carrying out the following steps from (e) to (j), if the occurrence probability distributions of clusters are not known;

(c) entering an occurrrence probability distributions of clusters; and

(d) classifying the defects in clusters having the highest occurrence probability;

(e) calculating a distance between optional defects in a characteristic space;

(f) assuming that one defect forms one cluster respectively in an initial state;

(g) setting a threshold;

(h) combining some clusters in one cluster if the distance between nearest defects included in the clusters, is less than the threshold;

(i) increasing the threshold somewhat; and

(j) repeating steps (h) and (i) until the threshold exceeds a predetermined upper limit or the number of the clusters in less than a predetermined lower limit.