In the semiconductor industry, the inspection of wafer map defects is crucial. WeLinkirt provides an effective solution to the challenges of wafer map defect inspection with advanced AI vision technology.
User Scenario: In the wafer manufacturing production line of a leading semiconductor manufacturer, wafers are the core products, and various defects on the wafer maps are the inspection objects. During the wafer production process, high - precision inspection of the micro - structure and patterns on the wafer surface is required to ensure that the quality and performance of the wafers meet the standards.
Pain Points: Traditional inspection methods face many quantitative challenges. The missed - detection rate is relatively high, about 3%, which causes some defective wafers to flow into subsequent processes, increasing production costs and product risks. The false - alarm rate also reaches 20%. Frequent false alarms not only increase labor costs but also reduce production efficiency. In addition, the change - over time is long, taking 30 minutes each time, which seriously affects the flexibility of the production line and the production rhythm.
Technical Principles
WeLinkirt adopts advanced deep - learning algorithms and high - precision imaging technology. The deep - learning algorithm is trained with a large number of wafer map samples and can learn the features and rules of different defect patterns. In terms of imaging, a self - developed 3D camera is used for data collection, combined with 3D morphology reconstruction technology, which can obtain detailed 3D information on the wafer surface. This method is effective because the deep - learning algorithm has powerful feature extraction and classification capabilities, which can accurately identify various tiny defects. The 3D camera and 3D morphology reconstruction technology can provide more comprehensive information, avoiding the possible missed - detection problems of traditional 2D inspection methods.
- The deep - learning algorithm can automatically learn defect features, improving the accuracy and stability of inspection.
- The self - developed 3D camera can obtain 3D information on the wafer surface to detect hidden defects.
- The 3D morphology reconstruction technology can accurately analyze the micro - structure of the wafer.
- Through training with a large number of samples, the model can adapt to different types of wafers and defect patterns.
WeLinkirt Solutions and Product Introduction
The DaoAI AI AOI software system and the DaoAI 2D / 3D AI AOI device provided by WeLinkirt are the keys to solving this problem. The DaoAI AI AOI software system has the feature recognition ability of the visual basic model. With only 1 - 20 good wafers, it can realize 0 - code automatic programming in 5 minutes. It also uses APDT positive - sample/few - sample learning and semantic false - alarm filtering technology, which can effectively reduce false alarms. The DaoAI 2D / 3D AI AOI device combines the self - developed 3D camera and 3D morphology reconstruction technology, which can detect hidden solder joints, coplanarity and micrometer - level morphology, improving the accuracy and comprehensiveness of inspection. During the implementation process, the device is integrated with the production line, deployed through SDK / API / Docker, and supports 100% local privatization to ensure that data does not leave the factory.
WeLinkirt's solution brings a new, efficient and accurate option for semiconductor wafer map defect inspection.
Quantitative Results: By using WeLinkirt's solution, the defect detection rate of wafer maps in this semiconductor manufacturer has increased to 97%, and the missed - detection rate has decreased to <3%. The false - alarm rate has decreased by - 60%, greatly reducing labor costs and the workload of re - inspection. The change - over time has been shortened to 5 minutes, and the flexibility of the production line and the production rhythm have been significantly improved.