The ACF conductive particle indentation+foreign object inspection machine is used to detect the ACF conductive particle indentation and bonding foreign objects on the COG and FOG parts of the bonding section of the LCD panel, including the number, distribution, indentation strength, deviation, and various foreign objects of the conductive particles. Its core technology is the use of infrared and differential interferometry techniques, using a line scan camera to optically image the bump area on the COG and FOG of the liquid crystal panel. The image is analyzed using traditional image algorithms and AI deep learning algorithms to ultimately confirm whether the target detection object is qualified.

The particle count re evaluation system is used to analyze the detection report data (particle count) of friendly particle indentation inspection machines. For products with particle counts less than the set threshold, traditional image algorithms and AI deep learning systems are used for secondary re evaluation, greatly improving the detection accuracy. The real NG data is uploaded through CIM and locked.