DEFECT DETECTION USING COHERENT LIGHT ILLUMINATION AND ARTIFICIAL NEURAL NETWORK ANALYSIS OF SPECKLE PATTERNS

• US 20190318469A1
• Filed: 08/10/2018
• Published: 10/17/2019
• Est. Priority Date: 04/17/2018
• Status: Abandoned Application

• Current Assignee: Coherent AI LLC
• Original Assignee: Coherent AI LLC
• Inventors: WANG, Xingze
• Application Number:
  US16/101,384
• Publication Number:
  US 20190318469A1
• Time in Patent Office: Days
• CPC Class Codes: G01B 11/162 by speckle- or shearing interferometry
• CPC Class Codes: G01N 2021/479 Speckle
• CPC Class Codes: G01N 2021/8883 involving the calculation of gauges, generating models
• CPC Class Codes: G01N 2021/8887 based on image processing techniques
• CPC Class Codes: G01N 21/4788 Diffraction for sizing particles G01N15/0205
• CPC Class Codes: G01N 21/8806 Specially adapted optical and illumination features
• 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: G01N 2201/06113 Coherent sources; lasers
• CPC Class Codes: G01N 2201/1296 using neural networks
• CPC Class Codes: G02B 21/0032 Optical details of illumination, e.g. light-sources, pinholes, beam splitters, slits, fibers G02B21/0036 - G02B21/008; means for illumination of specimens in general G02B21/06
• CPC Class Codes: G02B 27/48 Laser speckle optics
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 7/0004 Industrial image inspection

Abstract

A system and method for detecting defects in an object includes illuminating the object with a coherent light, recording the a speckle pattern of the coherent light reflected and/or scattered and/or transmitted from the object, and analyzing the speckle pattern using a trained artificial neural network to determine whether defects are present in the object and the types of defects. To train the neural network, sample objects having known types of defects or no defects are illuminated with a coherent light and the speckle patterns are recorded. The speckle patterns are labeled with the type of defects in the corresponding sample objects, and used as training data to train the network. The technique analyzes the speckle patterns directly, and does not require phase recovery and object shape reconstruction. The technique is useful for defect inspection in industrial production to detect defects such as scratches, air bubbles, deformation, stains, etc.

Claims

1. A system for detecting defects in a test object, comprising:

a coherent light source generating a coherent illumination light, the coherent light source being positioned to illuminate the test object with the coherent illumination light;

a two-dimensional image sensor, positioned to record a light pattern of the coherent illumination light after the coherent illumination light has interacted with the test object, the light pattern containing a speckle pattern; and

a data processing apparatus coupled to the image sensor, the data processing apparatus implementing a trained artificial neural network configured to analyze the light pattern to determine whether any defect is present in the test object and at least one type of the defect that is present.

2. The system of claim 1, wherein the artificial neural network has been trained to determine whether one or more types of defects selected from the following group are present in the test object:

scratches, concave or convex deformations, air bubbles, color variation, microscopic unevenness, stains, and chipping.

3. The system of claim 1, wherein the trained artificial neural network is configured to analyze the light pattern to determine presence and types of defects in the test object without performing phase recovery or object shape reconstruction.

4. The system of claim 1, wherein the image sensor is positioned to record a light pattern of the coherent illumination light that has been reflected and/or scattered by the test object.

5. The system of claim 1, wherein the image sensor is positioned to record a light pattern of the coherent illumination light that has transmitted through the test object and has been scattered by interior structures of the test object.

6. The system of claim 1, wherein the two-dimensional image sensor is a charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) detector without imaging optics.

7. The system of claim 1, wherein the two-dimensional image sensor is a camera.

8. The system of claim 1, wherein the two-dimensional image sensor includes a camera and imaging optics.

9. A method for detecting defects in a test object, comprising:

illuminating the test object with a coherent illumination light;

by a two-dimensional image sensor, recording a light pattern of the coherent illumination light after the coherent illumination light has interacted with the test object, the light pattern containing a speckle pattern; and

by a data processing apparatus which implements a trained artificial neural network, analyzing the light pattern to determine whether any defect is present in the test object and at least one type of the defect that is present.

10. The method of claim 9, wherein the artificial neural network has been trained to determine whether one or more types of defects selected from the following group are present in the test object:

scratches, concave or convex deformations, air bubbles, color variation, microscopic unevenness, stains, and chipping.

11. The method of claim 9, wherein the analyzing step is performed without performing phase recovery or object shape reconstruction.

12. The method of claim 9, wherein the recording step includes recording a light pattern of the coherent illumination light that has been reflected and/or scattered by the test object.

13. The method of claim 9, wherein the recording step includes recording a light pattern of the coherent illumination light that has transmitted through the test object and has been scattered by interior structures of the test object.

14. A method for detecting defects in a test object, comprising:

obtaining a plurality of sample objects, each sample object either having no defects or having known types of defects;

illuminating each sample object with a coherent illumination light;

for each sample object being illuminated, using a two-dimensional image sensor, recording a light pattern of the coherent illumination light after the coherent illumination light has interacted with the sample object, the light pattern containing a speckle pattern, to obtain a plurality of light patterns corresponding to the plurality of sample objects;

labeling each light pattern with at least one label indicating the type of types of defects or an absence of defects in the corresponding sample object, to generate a plurality of labeled light patterns;

obtaining an untrained artificial neural network implemented in a data processing apparatus;

training the untrained artificial neural network using the plurality of labeled light patterns as training data, to produce a trained artificial neural network;

illuminating the test object with a coherent illumination light;

using a two-dimensional image sensor, recording a light pattern of the coherent illumination light after the coherent illumination light has interacted with the test object, the light pattern containing a speckle pattern; and

using a data processing apparatus which implements the trained artificial neural network, analyzing the light pattern to determine whether any defect is present in the test object and at least one type of the defect that is present.

15. The method of claim 14, wherein the plurality of sample objects include a plurality of sample objects having no defects and a plurality of sample objects each having one or more defects selected from the following group:

scratches, concave or convex deformations, air bubbles, color variation, microscopic unevenness, stains, and chipping.

16. The method of claim 14, wherein the analyzing step is performed without performing phase recovery or object shape reconstruction.

17. The method of claim 14, wherein the recording step records a light pattern of the coherent illumination light that has been reflected and/or scattered by the test object.

18. The method of claim 14, wherein the recording step records a light pattern of the coherent illumination light that has transmitted through the test object and has been scattered by interior structures of the test object.