Detecting mura defects in master panel of flat panel displays during fabrication

Patent metadata

Field	Value
Publication number	US-10867382-B2
Application number	US-201916368989-A
Country	US
Kind code	B2
Filing date	Mar 29, 2019
Priority date	May 24, 2018
Publication date	Dec 15, 2020
Grant date	Dec 15, 2020

Abstract

A method is provided for detecting mura defects in a master panel during fabrication, the master panel containing multiple flat screen displays. The method includes preparing a combined image from image data of the master panel; enhancing the quality of the combined image, including removing artifacts from the combined image; filtering the enhanced quality combined image to detect local mura defects, the local mura defects including at least one structured pattern of defined geometric shapes; applying different candidate patterns to the filtered combined image; selecting one of the candidate patterns as a defect detection pattern, the defect detection pattern being closest to the structured pattern of defined geometric shapes of the detected local mura defects; and displaying at least a portion of the defect detection pattern on a display, together with the quality-enhanced combined image.

First claim

The invention claimed is:

1. A method of detecting mura defects in a master panel during fabrication, the master panel containing a plurality of flat screen displays, the method comprising:
preparing a combined image from image data of the master panel;
enhancing quality of the combined image, wherein enhancing the quality comprises removing artifacts from the combined image;
filtering the enhanced quality combined image to detect local mura defects, the local mura defects comprising at least one structured pattern of defined geometric shapes;
applying a plurality of different candidate patterns to the filtered combined image;
selecting one of the plurality of candidate patterns as a defect detection pattern, the defect detection pattern closest to the structured pattern of defined geometric shapes of the detected local mura defects being selected from among the plurality of candidate patterns; and
displaying at least a portion of the defect detection pattern on a display, together with the quality-enhanced combined image, to show positions of the detected local mura defects in the structured pattern of defined geometric shapes.

2. The method of claim 1 , wherein filtering the enhanced quality combined image to detect local mura defects comprises:
filtering out relevant spatial frequencies corresponding to length scales of the detected local mura defects to provide a first filtered image; and
convoluting the first filtered image with a set of templates corresponding to the defined geometric shapes, respectively.

3. The method of claim 1 , wherein the defined geometric shapes comprise spots and/or a plurality of rings.

4. A method of detecting mura defects in flat screen displays, prior to separation from a master panel during a fabrication process, the method comprising:
obtaining a plurality of images of a master panel, and storing image data corresponding to at least some of the plurality of images, wherein the image data comprise pixel signals received from a matrix of pixels on the master panel;
combining the stored image data of the plurality of images to provide a combined image;
filtering the combined image to detect mura defects using at least one defect pattern specific template for detecting a structured pattern of defined geometric shapes corresponding to mura defects;
creating candidate patterns, having differently dimensioned two-dimensional coordinate systems, respectively, that attempt to illustrate positioning of the detected mura defects;
selecting a defect detection pattern from among the created candidate patterns and a scan offset that most closely corresponds to the positioning of the detected mura defects, the selected defect detection pattern providing a strongest signal of a total filter response from the filtered combined image;
fine-tuning the selected defect detection pattern;
quantifying the detected mura defects using the fine-tuned defect detection pattern; and
performing quality assurance on the master panel based on the quantified detected mura defects.

5. The method of claim 4 , wherein each of the candidate patterns comprises horizontal and vertical periodicity of the detected mura defects, and horizontal and vertical scan offset, thereby creating a rectangular pattern.

6. The method of claim 5 , wherein at least one of the candidate patterns is rotated by introduction of a rotation angle parameter.

7. The method of claim 4 , wherein obtaining the plurality of images of the master panel comprises scanning the master panel using a scan head, wherein electrical signals or optical signals from the scan head connect to the pixels on the master panel, respectively, in a predetermined sequence to provide the pixel signals.

8. The method of claim 4 , wherein the master panel comprises a glass substrate.

9. The method of claim 4 , wherein combining the stored image data of the plurality of images to provide the combined image comprises:
down-sampling the plurality of images to reduce storage requirements for storing the image data;
arranging the down-sampled plurality of images in a two-dimensional pattern, and combining the two-dimensional pattern into a single larger image;
correcting the down-sampled plurality of images of the single larger image for contrast and background level to provide the combined image, which appears homogeneous; and
marking in a separate binary matrix regions of the combined image consisting of actual data, as opposed to unknown regions, wherein the binary matrix regions are used to weigh filter responses from filtering the combined image.

10. The method of claim 9 , further comprising cleaning up the combined image before filtering the combined image to detect the mura defects, cleaning up the combined image comprising:
removing measurement artifacts from the stored image data before combining the two-dimensional pattern into the single larger image; and
suppressing artifacts in individual frames of the single larger image before correcting for contrast and background level.

11. The method of claim 10 , wherein suppressing artifacts in individual frames of the single larger image comprises removing local overshoot and/or undershoot of signals in corners of the stored image data corresponding to the at least some of the plurality of images.

12. The method of claim 10 , wherein removing the measurement artifacts from the stored image data comprises:
removing noise originating from electronic amplifier components;
removing white noise using a low-pass filter; and
flattening the image by subtracting a local average extracted by convoluting the image with a Gaussian kernel.

13. The method of claim 12 , wherein removing the noise originating from electronic amplifier components comprises:
removing drift by subtraction of a gliding average;
removing changes in gain by normalizing signal strength;
removing crosstalk between amplifier channels; and
removing narrowband noise by applying narrowband spatial frequency filters.

14. The method of claim 4 , wherein prior to filtering with the at least one defect pattern specific template, the method further comprising:
filtering the combined image with a kernel that selects relevant spatial frequencies; and
compiling a histogram of the filtered combined image, and renormalizing the filtered combined image on a per-pixel signal basis as probability of not being noise by comparing pixel signals of the filtered combined image, at least in part, to the histogram.

15. The method of claim 14 , wherein after filtering with the at least one defect pattern specific template, the method further comprising:
convoluting the renormalized filtered combined image with a set of templates corresponding to the defined geometric shapes;
weighting a result of the convolution by a number of pixel signals that are actually data, as opposed to pixel signals of zero corresponding to pixels that lie in regions where no image data was acquired; and
smoothening the weighted result with the kernel to provide a filtered combined image, the smoothing comprising penalizing isolated signals and enforcing signals that match a predetermined feature length scale.

16. The method of claim 15 , wherein creating the candidate patterns comprises:
determining a range of plausible differently dimensioned two-dimensional coordinate systems; and
scanning the two-dimensional coordinate systems over the smoothened filtered combined image, removing local pixel signals at intersections of each of the two-dimensional coordinate systems, and measuring a combined signal strength in the removed local pixel sign

Assignees

• Keysight Technologies Inc

Inventors

• Moertelmaier Manuel
• Ura Tomonori
• Komma Yosuke
• Dieudonne Michael

Classifications

• G06F3/147

  using display panels · CPC title

• G01N21/95

  characterised by the material or shape of the object to be examined (G01N21/89 - G01N21/91, G01N21/94 take precedence) · CPC title

• G01N2021/8887

  based on image processing techniques · CPC title

• G01N21/8851 Primary

  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 title

• G09G2330/12

  Test circuits or failure detection circuits included in a display system, as permanent part thereof · CPC title