Name
Abstract | ||
|---|---|---|
In the semiconductor manufacturing industry, gold wires are commonly used to connect integrated circuits for transmitting signal and power. Hence, gold wire bonding quality inspection is a vital task for the functional stability of the integrated circuit chip. The shape of each gold wire bonding is one of the most important factors influencing the stability of chip performance. However, as the size of gold wire is quite small (around 0.02 mm), it is difficult to quantitatively measure the shape, i.e., the size-related indices. In this article, we propose a novel framework for fully automated gold wire bonding size-related measurement, which is the key step in gold wire bounding quality inspection. First, we employ a 3-D scanning device to obtain the 3-D point cloud of the chip area where gold wire bonding structures are. We then propose a 3-D deep learning-based algorithm to extract multiple gold wire bounding structures from the whole scanned point cloud. With the extracted points, a postprocessing method is introduced to group the gold wire-solder joint pairs. Moreover, we design a point cloud-based calculation model to perform the final size-related measurement. The proposed automated framework achieves high-accuracy quantitative measurement of gold wire bonding structures on the scale of 0.02 mm for the first time. The effectiveness of the proposed measurement framework is demonstrated by the experiments. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TIE.2021.3120473 | IEEE Transactions on Industrial Electronics |
Keywords | DocType | Volume |
3-D deep learning,gold wire bonding,point cloud processing,size-related measurement | Journal | 69 |
Issue | ISSN | Citations |
11 | 0278-0046 | 0 |
PageRank | References | Authors |
0.34 | 10 | 6 |