Advanced packaging is one of the key technologies in the continuous development of the semiconductor industry. As an important part of advanced packaging, micro-bumps (bump) directly affect the performance and reliability of chips. This article delves into how DaoAI's 3D AI-AOI detection technology solves the problems in the inspection of advanced packaging micro-bumps.
In the semiconductor industry, the development of advanced packaging technology is changing with each passing day. It plays a crucial role in improving the performance, integration, and reducing the power consumption of chips. Micro - bumps (bump) are the key structures for chip interconnection in advanced packaging, and their quality directly determines the reliability of subsequent interconnections. With the continuous advancement of technology, the pitch of micro-bumps in advanced packaging factories has entered the micron level, and the micro-bump arrays have become more dense. The quality of these micro-bumps, such as whether there are missing, bridging (adjacent bumps are connected together), and insufficient height, has a huge impact on the final performance of the chip. Once these defects are not detected in time during the production process and are found after subsequent processes such as chip stacking and bonding, it will lead to a multiple increase in rework costs.
Pain Point: Why is it Difficult?
From the perspective of quantifying detection indicators, traditional 2D AOI inspection has serious problems in detecting missing micro-bumps, bridging, and insufficient height. In the detection of missing micro-bumps, since 2D grayscale images cannot accurately reflect the actual height information of micro-bumps, the missed detection rate may be as high as 10% -15%. For bridging defects, due to the interference of coating reflection, the false alarm rate can reach 20% -30%. In the detection of insufficient height, because the 2D grayscale image from a top-view is almost the same as that of normal micro-bumps, the risk of missed judgment is extremely high, and the missed detection rate may be between 15% -20%.
The root cause of these problems lies in the limitations of 2D grayscale images themselves. 2D grayscale images can only present the planar grayscale information of objects and cannot provide accurate data in the height dimension. In the inspection of micro-bumps, the reflection of the coating will form a high-brightness area on the 2D grayscale image, which is easily misread as a bridging phenomenon. For micro-bumps with insufficient height, their grayscale features are similar to those of normal micro-bumps from a top-view, making it difficult to accurately distinguish them, resulting in missed judgments. To reduce the missed detection rate, traditional 2D AOI can only adjust the detection threshold strictly, but this will also cause normal micro-bumps to be over-judged as defects, greatly increasing the over-kill rate, and falling into a dilemma of missed judgment and over-kill.
Technical Principle
DaoAI's 3D AI-AOI inspection equipment uses advanced micron-level 3D topography data acquisition technology. The equipment can accurately obtain the height and volume information of each micro-bump through special imaging hardware. Its hardware mechanism is based on advanced optical principles, and through multi-angle light irradiation and image acquisition, it constructs the 3D topography of micro-bumps. At the algorithm level, the equipment uses advanced image processing and deep learning algorithms to analyze and process the collected 3D data.
Compared with the traditional 2D AOI method, 3D AI-AOI has significant advantages. Traditional 2D AOI relies on grayscale images that are easily interfered by reflection for judgment, while 3D AI-AOI directly determines the state of micro-bumps based on 3D topography data. For missing micro-bumps, the height is zero in the 3D data; when there is bridging, the contours of adjacent bumps will be adhered together; insufficient height is reflected as being lower than the set reference height, and these features can be clearly determined in the 3D data, without being affected by the reflection of the coating. The AI-ADC module further classifies the detected defects. For rare-shaped defects, the APDT positive sample learning method is used for supplementation, which improves the recognition ability of various defects.
Typical Application Scenarios
- Missing micro-bump detection: In the micro-bump manufacturing process, there may be cases of missing micro-bumps due to process problems. The 3D AI-AOI inspection equipment can accurately collect the height information of micro-bumps. When the height of a certain position is detected to be zero, it can be determined that the micro-bump is missing. The difficulty lies in the extremely small pitch of micro-bumps, which requires the equipment to have high-precision height detection ability to accurately distinguish between normal micro-bumps and missing positions.
- Bridging defect detection: Bridging is a defect where adjacent micro-bumps are connected together, which will seriously affect the interconnection performance of the chip. 3D AI-AOI uses 3D topography data to observe whether the contours of adjacent micro-bumps are adhered. In actual inspection, the reflection of the coating may interfere with the judgment, but 3D AI-AOI is not affected by the reflection and can accurately identify bridging defects. The difficulty lies in accurately judging the adhesion of adjacent micro-bump contours to avoid false and missed judgments.
- Insufficient height detection: Insufficient height of micro-bumps will affect the interconnection reliability of the chip. 3D AI-AOI uses the set reference height as the standard, compares the collected height of micro-bumps with the reference height, and determines that the height is insufficient if it is lower than the reference height. The difficulty lies in determining a reasonable reference height and accurately detecting small height differences.
- Coplanarity anomaly detection: The coplanarity of micro-bumps is crucial for chip stacking and bonding. 3D AI-AOI can quantify and output the coplanarity information of micro-bumps. By analyzing the height data of multiple micro-bumps, it can determine whether they are on the same plane. The difficulty lies in the accurate analysis and processing of a large amount of micro-bump height data to identify coplanarity anomalies.
Implementation Case
An advanced packaging factory, which is relatively large-scale and has a complex production process, has a micro-bump pitch that has reached the micron level. Before introducing DaoAI's 3D AI-AOI inspection equipment, the factory had been using the traditional 2D AOI inspection method and was facing a relatively high false alarm rate and missed detection rate. During the implementation process, DaoAI's technical team first conducted a detailed investigation on the factory's production process and inspection requirements, and customized the configuration of the 3D AI-AOI inspection equipment according to the actual situation. During the equipment installation and commissioning phase, the technical team provided professional training to the operators to ensure that they could use the equipment skillfully.
After the equipment was put into operation, the inspection effect was significantly improved, greatly reducing the rework cost for the enterprise.
DaoAI's Solution and Product
DaoAI's 3D AI-AOI inspection equipment is a complete micro-bump inspection solution. The equipment can accurately detect various defects such as missing micro-bumps, bridging, insufficient height, and coplanarity anomalies by accurately collecting micron-level 3D topography data. The AI-ADC module can intelligently classify the detected defects, improving the inspection efficiency and accuracy. For rare-shaped defects, the APDT positive sample learning method can continuously supplement samples to enhance the equipment's recognition ability of various defects. The operation interface of the equipment is simple and intuitive, which is convenient for operators to use. At the same time, it has high-efficiency data processing ability to meet the inspection requirements of large-scale production.
Quantitative Results
After the 3D AI-AOI inspection equipment was put into operation, the micro-bump inspection results of the factory were remarkable. The detection rate of missing and bridging micro-bumps increased from less than 80% to more than 98%, greatly improving the defect detection accuracy. The false alarm rate caused by coating reflection decreased from 25% to less than 5%, a reduction of nearly 80%. At the same time, the dilemma of over-kill and missed judgment was effectively solved, preventing a large number of defects from flowing into subsequent processes, and significantly reducing the rework cost. These quantitative results fully demonstrate the strong advantages of DaoAI's 3D AI-AOI inspection equipment in the inspection of advanced packaging micro-bumps.
FAQ
What are the problems with 2D grayscale images in the inspection of advanced packaging micro-bumps?
In the inspection of advanced packaging micro-bumps, 2D grayscale images are prone to misreading the reflection of the coating as bridging and missing the detection of insufficient height as normal. Traditional 2D AOI tightens the threshold to reduce missed detections, which in turn increases the over-kill rate, falling into a dilemma, resulting in inaccurate inspection results and affecting production efficiency and cost.
How does DaoAI solve the problems in micro-bump inspection?
DaoAI deploys 3D AI-AOI inspection equipment to determine the state of bumps based on micron-level 3D topography data, avoiding false alarms caused by coating reflection. The AI-ADC classifies defects, and the APDT positive sample learning supplements rare-shaped defects, improving the accuracy and comprehensiveness of the inspection.
What are the effects after the 3D AI-AOI inspection equipment is put into operation?
After the equipment is put into operation, the detection rate of missing and bridging bumps has significantly increased to over 98%, and the false alarm rate caused by coating reflection has dropped by nearly 80%. It breaks the dilemma of over-kill and missed judgment, prevents defects from flowing into subsequent processes, and effectively reduces rework losses.
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.