The ITO line AOI inspection machine (also known as the step electrode automatic inspection machine, terminal scratch automatic inspection machine) is mainly used for defect detection of ITO in the exposed area of TFT-LCD lines, improving production line yield and the process control capability of manufacturers. Mainly detecting scratches, short circuits, open circuits, micro breaks, foreign objects (dirt), corrosion, burrs, etc. The equipment can be widely used in fields such as TFT-LCD industry, touch screen industry, photovoltaic industry, and LED industry, achieving rapid detection of ITO lines, silver paste lines, etc. on glass substrates or flexible substrates (PET, CPI, etc.).

TFT-LCD online foreign object line inspection machine is an AOI equipment used to detect product defects before and after bonding of LCD panels. It mainly detects foreign objects, corrosion, scratches and other defects, thereby improving production line yield and the manufacturer's process control capabilities.

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 TFT-LCD edge inspection machine is developed and produced by Fuxin Optoelectronics. Based on machine vision and AI artificial intelligence technology, the TFT-LCD edge inspection machine integrates optics, mechanics, electronics, software, and algorithms. It is mainly used for automatic detection of edge defects in the TFT-LCD production process, achieving machine replacement. In practical applications, suspected defect areas are extracted from the edge sample images of TFT-LCDs after image processing, and judgment criteria are established to effectively eliminate interference. The types of defects such as edge extension, shell, edge collapse, and corner collapse are successfully determined, and the criteria are established to avoid repeated judgments and detect multiple defects simultaneously.

The AVI inspection machine for appearance defects of 3C products is mainly used for defect detection of 3C product appearance, improving production line yield and the process control capability of manufacturers. The main application areas include mobile phones, laptops, tablets, LCD modules, wearable devices, televisions, etc.   The detectable defects include surface scratches, burrs, damages, pressure marks, broken edges, notches, hidden cracks, burrs, warping, foreign objects, dirt, white spots, bubbles, pinholes, bumps, etc.  

Desktop module appearance AVI inspection is mainly used for defect detection of 3C product appearance, improving production line yield and the process control capability of manufacturers. Mainly detecting scratches, damages, pressure marks, broken edges, gaps, hidden cracks, burrs, warping, foreign objects, dirt, white spots, bubbles, pinholes, bumps and depressions, as well as component errors, leaks, reversals, excess, skewers, offsets, color differences, sizes, segments, etc.

The wafer scratch AOI inspection machine is mainly used for detecting obvious scratch defects such as missed scratches, defocusing, uncut, and bicrystals on the core grains on the Disco ring after wafer splitting machine processing. Through Machine Vision defect detection, the presence or absence of the above defects can be detected to distinguish between good and defective products, reduce manual labor, and improve detection efficiency.

This device measures the thickness of wafers and display panels after partial processes such as wafer slicing, grinding, thinning, and slicing. Can synchronously test the TTVBOWWRAP/SORI and other parameter errors of the material itself.

Solid state wire bonding AOI inspection machine, also known as wire bonding AOI inspection machine, is mainly used for efficient AOI inspection of defects after die bonding and wire bonding in the semiconductor packaging and testing field. It has the characteristics of high speed, high precision, and high inspection coverage. Optical system modules and core detection algorithms with completely independent intellectual property rights, as well as AI deep learning algorithms, are suitable for detecting various defects on grain surfaces, solder joints, solder wires, and frame surfaces that occur during the solidification and wire bonding processes.

Automatic soldering machines are mainly used in the electronic manufacturing industry, targeting soldering processes that are difficult for production equipment to complete such as reflow soldering and wave soldering. They are particularly suitable for soldering sensitive components in mixed circuit boards, thermal sensitive components, and SMT post-processing. Automatic soldering robots replace manual soldering, improving work efficiency and welding quality. Our AOI visual automatic soldering machine has added AOI defect detection with AI deep learning function on the basis of the original automatic soldering machine. It can mainly detect defects such as solder leakage, continuous soldering, insufficient soldering, and excessive soldering, monitor the soldering quality in real time, and improve the yield rate. The AOI visual inspection module can be integrated with friendly soldering machines to add AOI defect detection functionality. This module can also achieve CCD visual positioning, quickly and automatically finding solder joints, thereby saving programming time for single point positioning.

At present, most of the existing traditional AOI detection equipment in the market does not have AI deep learning function, and generally has pain points such as high false alarm rate, which cannot achieve automated production and low production efficiency. The AI artificial intelligence system communicates with friendly AOI devices through the network, and based on the detection results of friendly AOI devices, uses a deep learning based AI artificial intelligence system for re evaluation to reduce false alarm rates and improve detection accuracy.

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.