Method for localizing and isolating an errant process step

• US 6,535,776 B1
• Filed: 09/20/1999
• Issued: 03/18/2003
• Est. Priority Date: 09/20/1999
• Status: Expired due to Term

• Current Assignee: Lockheed Martin Energy Research Corporation (Martin Marietta Corporation)
• Original Assignee: UT-Battelle LLC
• Inventors: Karnowski, Thomas P., Tobin, Kenneth W. Jr., Ferrell, Regina K.
• Primary Examiner(s): Picard, Leo
• Assistant Examiner(s): JARRETT, RYAN A
• Application Number:
  US09/399,019
• Time in Patent Office: 1,275 Days
• Field of Search: 700/108-110, 700/121, 700/51, 382/141, 382/145, 382/149, 382/228, 702/84, 702/180, 702/185, 707/6
• US Class Current: 700/110
• CPC Class Codes: G01N 2021/8854 Grading and classifying of flaws
• 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: G01R 31/302 Contactless testing G01R31/66 takes precedence
• CPC Class Codes: G06T 2207/30124 Fabrics; Textile; Paper
• CPC Class Codes: G06T 2207/30141 Printed circuit board [PCB]
• CPC Class Codes: G06T 2207/30164 Workpiece; Machine component
• CPC Class Codes: G06T 7/001 using an image reference approach
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps

Abstract

A method for localizing and isolating an errant process includes the steps of retrieving from a defect image database a selection of images each image having image content similar to image content extracted from a query image depicting a defect, each image in the selection having corresponding defect characterization data. A conditional probability distribution of the defect having occurred in a particular process step is derived from the defect characterization data. A process step as a highest probable source of the defect according to the derived conditional probability distribution is then identified. A method for process step defect identification includes the steps of characterizing anomalies in a product, the anomalies detected by an imaging system. A query image of a product defect is then acquired. A particular characterized anomaly is then correlated with the query image. An errant process step is then associated with the correlated image.

Claims

1. A method for localizing and isolating an errant process step in multi-step processes, comprising the steps of:

retrieving from a defect image database a selection of images, each said image having image content similar to image content extracted from a query image depicting a defect, each said image in said selection having corresponding defect characterization data; and

directly deriving from said defect characterization data a conditional probability distribution of said defect having occurred in at least one process step;

wherein a particular process step from said process steps can be identified as a highest probable source of said defect based on said conditional probability distribution.

2. The method according to claim 1, wherein the retrieving step comprises the steps of:

providing to a content-based image retrieval engine, a query image depicting a defect;

retrieving from said content-based image retrieval engine, a selection of images, each said image having image content similar to image content extracted from said query image; and

,

ranking said selection of images according to a similarity metric.

3. The method according to claim 1, wherein the deriving step comprises the steps of:

calculating a process step conditional probability distribution from said defect characterization data;

calculating a defect class conditional probability distribution from said defect characterization data;

selecting a process step included in said process step conditional probability distribution having a highest probability;

selecting a defect class included in said defect class conditional probability distribution having a highest probability; and

,

merging said selected process step with said selected defect class to produce a probable source process step of said defect.

4. The method according to claim 1, wherein the identifying step comprises the steps of:

ordering a ranked list of probable source process steps of said defect; and

,

reporting to a user said ranked list,

whereby said ranked list localizes and isolates a probable source process step of said defect.

5. A method for process step defect identification comprising the steps of:

characterizing anomalies in a product said anomalies detected by a content-based imaging system, wherein the characterizing step comprises the steps of detecting a product anomaly passing within imaging range of a computer vision system;

forming an image of said product anomaly;

assigning defect characterization data to said formed image; and

, storing said formed image and said assigned image defect characterization in a defect characterization database, whereby each formed image and assigned image defect characterization in said defect characterization database can be retrieved for subsequent comparison to a query image depicting a product anomaly;

acquiring a query image of a product defect;

correlating a particular characterized anomaly with said query image, wherein the correlating step comprises the steps of retrieving from said defect characterization database, a selection of images, each image having image content similar to image content extracted from said query image;

ranking said selection of images according to a similarity metric wherein the ranking step comprises the steps of;

retrieving from said defect characterization database, defect characterization data corresponding to said selection of images;

deriving from said defect characterization data a conditional probability distribution of said defect having occurred in a particular process step; and

, identifying a process step as a highest probable source of said defect according to said derived conditional probability distribution, and,

identifying an errant process step associated with said correlated image.

6. The method according to claim 5, wherein the deriving step comprises the steps of:

calculating a process step conditional probability distribution from said defect characterization data;

calculating a defect class conditional probability distribution from said defect characterization data;

selecting a process step included in said process step conditional probability distribution having a highest probability;

selecting a defect class included in said defect class conditional probability distribution having a highest probability; and

,

merging said selected process step with said selected defect class to produce a probable source process step of said defect.

7. A computer apparatus programmed with a routine set of instructions stored in a fixed medium comprising:

means for retrieving from a defect image database a selection of images, each image having image content similar to image content extracted from a query image depicting a defect, each said image in said selection having corresponding defect characterization data;

means for directly deriving from said defect characterization data a conditional probability distribution of said defect having occurred in at least one process step; and

,

means for identifying a process step as a highest probable source of said defect based on said derived conditional probability distribution.

8. The computer apparatus according to claim 7, wherein the retrieving means comprises:

means for providing to a content-based image retrieval engine, a query image depicting a defect;

means for retrieving from said content-based image retrieval engine, a selection of images, each image having image content similar to image content extracted from said query image; and

,

means for ranking said selection of images according to a similarity metric.

9. The computer apparatus according to claim 7, wherein the deriving means comprises:

means for calculating a process step conditional probability distribution from said defect characterization data;

means for calculating a defect class conditional probability distribution from said defect characterization data;

means for selecting a process step included in said process step conditional probability distribution having a highest probability;

means for selecting a defect class included in said defect class conditional probability distribution having a highest probability; and

,

means for merging said selected process step with said selected defect class to produce a probable source process step of said defect.

10. The computer apparatus according to claim 7, wherein the identifying means comprises:

means for ordering a ranked list of probable source process steps of said defect; and

,

means for reporting to a user said ranked list,

whereby said ranked list localizes and isolates a probable source process step of said defect.