3D AI AOI Equipment Accurately Detects Hidden Solder Joints of BGA/QFN

With the in - depth development of intelligent manufacturing, industrial vision quality inspection has become more and more prominent in the electronics/PCBA industry. In particular, the detection of hidden solder joints under BGA/QFN packaging plays a key role in ensuring product quality and performance. WeLinkirt's 3D AI AOI equipment brings an innovative solution to this field.

Scenario: In the electronics/PCBA industry, intelligent manufacturing is upgrading from single-point applications to a systematic level. In this process, the importance of industrial vision quality inspection is increasing. Take the PCBA production line of a leading electronics manufacturer as an example. After the BGA/QFN packaging process, the detection of hidden solder joints on the circuit board has become a key step in improving product quality. BGA (Ball Grid Array) and QFN (Quad Flat No - lead) are common integrated circuit packaging forms. The quality of their hidden solder joints is directly related to the performance and reliability of electronic products. Once there are problems with these hidden solder joints, it may lead to product failures, shorten the service life, and even affect the stability of the entire system, causing huge losses to the enterprise.

Pain Points: Why is it Difficult?

Traditional 2D optical inspection methods face many difficulties when detecting hidden solder joints under BGA/QFN packaging. From a quantitative perspective, the false-negative rate is about 3%, which means that out of every 100 products, 3 defective products may flow into the market. This not only damages the company's reputation but also may lead to a series of problems such as after-sales maintenance, increasing additional costs. The false-positive rate is as high as 25%, which requires a large amount of manpower for re-inspection, greatly increasing production costs and extending the production cycle. In addition, when changing product models, the programming time is up to 30 minutes, which seriously affects production efficiency in a fast-paced production environment.

So, why are traditional methods not competent? This is due to the particularity of BGA/QFN packaging. The hidden solder joints are inside the package, and 2D optical inspection can only obtain the planar image information of the solder joints, without knowing their three-dimensional shape. Key information such as the height and shape of the solder joints is ignored in 2D inspection, resulting in an inability to accurately determine whether there are defects such as false soldering and air holes in the solder joints. Moreover, 2D inspection is not sensitive to small surface changes and is difficult to detect micron-level morphological defects, which is also an important reason for the high false-negative rate.

Technical Principle

WeLinkirt's 3D AI AOI equipment uses a self-developed 3D camera for imaging. The camera uses the structured light principle to project a specific light pattern onto the surface of the detection object. When the light hits the object surface, due to the unevenness of the object surface, the reflected light will be deformed. By analyzing the deformation of the reflected light, the device can obtain the three-dimensional information of the object. After obtaining the three-dimensional data, the device performs three-dimensional morphological reconstruction and converts it into point cloud data. The point cloud data contains the three-dimensional coordinate information of each point on the object surface and can clearly present the three-dimensional shape of the solder joint, including height, shape, etc.

Compared with traditional 2D optical inspection methods, 3D point cloud data has obvious advantages. 2D inspection has blind spots and cannot detect the internal conditions of hidden solder joints, while 3D point cloud data can present the entire picture of the solder joint. By analyzing this three-dimensional information, the device can accurately detect defects in hidden solder joints, such as false soldering and air holes. When detecting coplanarity, 3D point cloud data can accurately measure the height difference of solder joints to determine whether the coplanarity requirements are met. For the detection of micron-level morphology, the high precision of 3D imaging can capture small surface changes to ensure the accuracy of detection. In addition, the device also uses 2D-3D fusion technology, which combines the texture information of 2D images and the geometric information of 3D point clouds to further improve the comprehensiveness and accuracy of detection.

Typical Application Scenarios

• Detection of hidden solder joint defects: For hidden solder joints under BGA/QFN packaging, traditional 2D inspection is difficult to find internal defects such as false soldering and air holes. The 3D point cloud data of WeLinkirt's 3D AI AOI equipment can clearly present the three-dimensional shape of the solder joint. By analyzing information such as height and shape, it can accurately determine whether there are defects in the solder joint. The difficulty lies in the special position of the hidden solder joint, which makes it difficult to obtain accurate three-dimensional information. The device requires high-precision imaging and analysis algorithms.
• Coplanarity detection: Coplanarity is crucial for the performance of electronic products. The device uses 3D point cloud data to accurately measure the height difference of solder joints and determine whether the coplanarity requirements are met. The difficulty lies in the need for high-precision measurement technology to ensure that small height differences can be detected.
• Micron - level morphology detection: Small surface changes in electronic products may affect their performance. The high precision of 3D imaging can capture micron-level morphological changes and detect surface defects such as scratches and bumps. The difficulty lies in the ability to distinguish normal small textures from defect features.
• Air hole detection: Air holes in solder joints can affect the strength and conductivity of the solder joint. 3D point cloud data can present the internal situation of the solder joint. By analyzing the distribution of the point cloud data, the presence of air holes can be detected. The difficulty lies in the different sizes and shapes of air holes, which require accurate identification.

Implementation Case

A large-scale electronic manufacturing enterprise has multiple PCBA production lines, and its products cover a variety of electronic products. In the hidden solder joint detection link after the BGA/QFN packaging process, the enterprise has been using the traditional 2D optical inspection method, facing problems such as high false-negative rate, high false-positive rate, and long programming time for product model change. After learning about WeLinkirt's 3D AI AOI equipment, the enterprise decided to conduct a pilot application.

During the implementation process, WeLinkirt's technical team conducted a detailed investigation and evaluation of the enterprise's production line, installed and debugged the equipment according to the actual needs of the enterprise. At the same time, professional training was provided to the enterprise's operators to ensure that they could use the equipment and software system proficiently. After a period of trial operation and optimization, the equipment was officially put into use.

After using WeLinkirt's 3D AI AOI equipment, the enterprise's inspection results have been significantly improved.

Before the implementation, the enterprise's false-negative rate was about 3%, the false-positive rate was as high as 25%, and the programming time for product model change was 30 minutes. After the implementation, the detection rate reached 99.2%, the false-negative rate was reduced to <0.8%, the false-positive rate was reduced by -68%, and the product model change time was shortened from 30 minutes to 5 minutes. The improvement of these data not only reduces the risk of defective products flowing into the market but also effectively reduces the manpower and time cost of re-inspection, significantly improving production efficiency.

WeLinkirt's Solution and Products

Centered around the 3D AI AOI equipment, WeLinkirt provides a complete inspection solution. The self-developed 3D camera and three-dimensional morphological reconstruction technology of the equipment can effectively detect hidden solder joints, coplanarity, micron-level morphology, and air holes and other 2D optical blind-spot defects under BGA/QFN packaging. The supporting DaoAI AI AOI software system has the feature recognition ability of the visual basic model. It can realize 0-code automatic programming for a good product in 5 minutes, adopts APDT positive-sample/few-sample learning (1-20 good products), and reduces false alarms through the semantic false-alarm filtering function.

Quantitative results: After using WeLinkirt's 3D AI AOI equipment, the detection rate reaches 99.2%, the false-negative rate is reduced to <0.8%, greatly reducing the risk of defective products flowing into the market. The false-positive rate is reduced by -68%, effectively reducing the manpower and time cost of re-inspection. The product model change time is shortened from 30 minutes to 5 minutes, significantly improving production efficiency.

This article was generated by AI. Customer cases are simulated scenarios based on real product capabilities and figures are illustrative; see product pages for official benchmarks.