Data acquisition system for OLED defect detection and augmentation of system data through diffusion model

Figures & data

Figure 1. RCNN-based segmentation.

Figure 2. (a) Artificial defect source image example; (b) Output device and (c) Camera sensors and mounts; (d) Acquired raw image (14,192 × 10,640).

Figure 3. Example of a defined defect classification; (a) Open (b) Line out (c) Short (d) Foreign bodies.

Figure 4. Result of the defect detection, row 1 is the 1024 × 1024 output data and row 2 is the zoomed-in image.

Table 1. Detection rate by defined defect type.

Defect type	# of detection	# of instances	Detection Rate (%)
Open	113	116	97.41
Short	213	215	99.06
Foreign bodies	39	40	97.5
Line-out	4	4	100
Total	369	375	98.4

Figure 5. Source data on the left, diffusion model on the right 3 images, OLED pattern information lost as the network process.

Figure 6. Generate OLED panel unclassified defect images with a partial diffusion model.

Table 2. Detecting unclassified defects in partial application models of diffusion models.

Target defects	System acquisition data learning model	Data learning models augmented by partial application of diffusion models
Defined defect detection	98.4%	98.6%
Unclassified defect detection	62.8%	92.85%