Name
Title | ||
|---|---|---|
An Adaptive Deep Learning Framework for Fast Recognition of Integrated Circuit Markings |
Abstract | ||
|---|---|---|
Fast recognition of integrated circuit (IC) markings is an essential but challenging task in electronic device manufacturing lines. This article develops an adaptive deep learning framework to facilitate the fast marking recognition of IC chips. The proposed framework contains four deep learning components, namely, chip segmentation, orientation correction, character extraction, and character recognition. The four components utilize different convolutional neural network structures to guarantee excellent adaptivity to a wide range of IC types and mitigate the influence of the low-quality chip images. In particular, the character extraction model is comprised of two improved label generation strategies and a proposed border correction method, so as to accommodate tiny scale chips and compactly printed markings. Experiments from the chip image dataset of a real laptop manufacturing line reached a recognition Precision of 91.73% and the Recall of 92.93%. The results demonstrate the superiority of the proposed framework to the state-of-the-art models and the effectiveness of handling a great diversity of chips with different scales, shapes, text fonts, marking colors, and layouts. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TII.2021.3093388 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
Adaptive deep learning,character detection,chip segmentation,convolutional neural network (CNN),integrated circuit (IC),marking recognition | Journal | 18 |
Issue | ISSN | Citations |
4 | 1551-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongshu Chen | 1 | 0 | 0.34 |
| Changhua Zhang | 2 | 0 | 1.01 |
| Lin Zuo | 3 | 0 | 0.68 |
| Tangfan Xiahou | 4 | 2 | 2.73 |
| Yu Liu | 5 | 190 | 19.09 |