Name
Abstract | ||
|---|---|---|
Acoustic Micro Imaging (AMI) techniques have been widely used as a non destructive evaluation (NDE) tool in evaluating both microelectronic packages and solder joints. However, there are a lack of analysis tools and automated inspection techniques to facilitate the inspection process. This paper presents a novel approach to extract the solder joint’s features from ultrasound images for automated solder joint assessment. The proposed method consists of three stages: solder joint detection, feature extraction, and defect evaluation. Firstly, a gradient based circular Hough transform is employed to detect the solder joints in acquired ultrasound images. Subsequently, feature extraction is carried out by a radial gradient based region growing algorithm. Finally, defect evaluation is achieved by analysing the variation of the image features automatically. Cross-sectional analysis was performed to validate and evaluate the performance of the solder joint inspection system. The results show that image features obtained by the system can be used easily to distinguish between a healthy and fractured joint, thus confirming the feasibility of the automated AMI inspection technique. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.microrel.2012.07.018 | Microelectronics Reliability |
Field | DocType | Volume |
Flip chip,Engineering drawing,Electronic engineering,Artificial intelligence,Automated optical inspection,Ultrasonic testing,Computer vision,Feature (computer vision),Microelectronics,Hough transform,Feature extraction,Soldering,Engineering | Journal | 52 |
Issue | ISSN | Citations |
12 | 0026-2714 | 3 |
PageRank | References | Authors |
0.54 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryan S. H. Yang | 1 | 3 | 0.54 |
| Derek R. Braden | 2 | 4 | 0.92 |
| Guang-Ming Zhang | 3 | 5 | 2.00 |
| David M. Harvey | 4 | 6 | 2.50 |