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Auto Visual Tester AVT-4030
Auto Visual Tester AVT-4030
Auto Visual Tester AVT-4030
The quality of the screen, as a mold for screen printing of solar cells, will directly affect the quality of the cells. The traditional manual observation method has problems such as low efficiency, slow detection speed and low accuracy.
Millennial Auto Visual Tester AVT-4030 integrates four major detection functions into one, which is used to detect the various characteristics of solar cell screens including: size, defects, tension, and film thickness.
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Integrated size, defect, film thickness, and tension detection

By concentrating the four processes on one piece of equipment, the four inspection data can also be unified into one form or system, simplifying the process and improving production efficiency.
After the screen is placed in the equipment, the size, defects, and film thickness can be automatically measured with a high degree of automation.

Stable and reliable structure

In order to ensure that the overall structure is stable and reliable, Millennial Auto Visual Tester AVT-4030 is equipped with a marble base and uses a 0.1μm grating ruler to achieve a line width measurement accuracy of 0.3μm and a PT value measurement accuracy of 2μm, improving the quality of the screen!

Precise dimensional feature detection

● Intelligent dimensional feature recognition and measurement
● Detect screen line width, line spacing, PT value, etc.
● Intelligent comparison with standard data of screen drawings

AI intelligent defect detection

Advanced software integration technology can collect and detect more defects and classify them according to product characteristics, achieving rapid and accurate detection of screen defects.
The minimum detection resolution is 2 μm, and the smallest defect can be detected within 5 μm, which can significantly reduce the intensity of manual inspection, effectively improve the screen printing yield rate, and significantly reduce quality risks and losses.
● Automatic difference comparison between screen version and drawing can be realized
● Automatically identify screen defect types based on AI deep learning
● Use AI deep learning to build a new defect model library

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