3D AI AOI Equipment Detects Semiconductor Bump Defects

In the semiconductor chip production process, the quality inspection of bumps is crucial. WeLinkirt's 3D AI AOI equipment provides an effective solution to the problem of bump defect detection with its advanced technology.

User Scenario: A leading semiconductor manufacturer needs to conduct quality inspections on bumps in the bump welding process of its chip packaging production line. As the key connection between the chip and the packaging substrate, defects such as missing or bridged bumps can seriously affect the performance and reliability of the chip. Therefore, accurate inspection of bumps is an important step in ensuring chip quality.

Pain Points: Under traditional inspection methods, the manufacturer faces many difficulties. The missed detection rate is relatively high, with about 3% of bump defects unable to be detected, causing some defective chips to flow into subsequent processes, increasing production risks and costs. At the same time, the false alarm rate reaches 15%. A large number of false alarm messages not only waste manpower for re-inspection but also reduce the production efficiency of the production line. In addition, the traditional inspection equipment has a long model-changing time, taking 30 minutes each time, which seriously affects the flexibility of the production line. Combining with today's hot topic, in the production of mechanical parts, these difficulties in defect detection will directly affect product quality and production efficiency and must be effectively solved.

Technical Principle

WeLinkirt's 3D AI AOI equipment uses a self-developed 3D camera for image acquisition. Its working principle is based on the structured light three-dimensional measurement technology. By projecting a specific structured light pattern onto the measured object, the camera captures the deformation of the pattern reflected from the object surface. Since the missing or bridging of bumps will cause changes in the surface topography, these changes will be reflected in the reflected pattern. The equipment uses a three-dimensional topography reconstruction algorithm to convert the collected deformation pattern data into three-dimensional point cloud data, thereby accurately restoring the real three-dimensional topography of the bumps.

• For defects in 2D optical blind spots such as hidden solder joints, coplanarity, and micron-level topography, the 3D point cloud data can provide more comprehensive information. By analyzing the 3D point cloud data, features such as the height, shape, and position of the bumps can be accurately identified, thus detecting defects such as missing and bridged bumps.
• The device also adopts 2D-3D fusion technology, combining the texture information of 2D images with the topography information of 3D point clouds, further improving the accuracy and reliability of defect detection.
• In terms of algorithms, the device uses advanced deep-learning algorithms to train a large number of bump samples, enabling the model to automatically learn the normal and defect features of bumps, thus achieving accurate classification and identification of bump defects.

WeLinkirt's Solution and Product

Centered around the 3D AI AOI equipment, WeLinkirt provides a complete defect-detection solution for the semiconductor manufacturer. With its self-developed 3D camera and advanced three-dimensional topography reconstruction technology, the equipment can detect various bump defects with high accuracy. The supporting DaoAI AI AOI software system has the feature-recognition ability of the visual basic model. It can complete zero-code automatic programming in 5 minutes with just one good sample. Through APDT positive-sample/few-sample learning (only 1-20 good samples are needed) and combined with the semantic false-alarm filtering function, false alarms are effectively reduced.

WeLinkirt's 3D AI AOI equipment provides an efficient and accurate solution for semiconductor chip bump inspection.

Quantitative Results: After using WeLinkirt's 3D AI AOI equipment, the manufacturer's defect detection rate has increased significantly to 99.2%, and the missed detection rate has decreased to <0.8%. The false alarm rate has been reduced by -68%, greatly reducing the manpower and time cost of re-inspection. At the same time, the equipment model-changing time has been shortened to 5min, improving the flexibility and production efficiency of the production line.