Name
Abstract | ||
|---|---|---|
The electronic industry has been experiencing a growing counterfeit market, resulting in electronic supply chains in other industries to be prone to counterfeit parts as well. Over the past few years, several methods have been developed for evaluating the reliability of an IC and distinguishing them as counterfeit or authentic. Trained experts offer services for evaluating an IC based on destructive or non-destructive methods. However, defect detection and recognition are mostly dependent on human decision, and therefore are vulnerable to error. In this paper, we propose a method to automatically detect and identify die-face delamination on an IC die. Die-face delamination is a predominant internal defect in recycled ICs but can be easily missed during defect detection. Here, we have acquired the 3D image of an IC non-destructively using X-ray computed tomography and applied image processing techniques and machine learning algorithms on the 3D image to detect die-face delamination in the forms of thermally induced cracks and damaged surfaces. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.microrel.2018.06.083 | Microelectronics Reliability |
Keywords | Field | DocType |
Automatic defect detection,IC counterfeit detection,3D image processing,Machine learning | Electronic industry,Image processing,Supply chain,Computed tomography,Artificial intelligence,Engineering,Counterfeit,Machine learning,3d image,Delamination | Journal |
Volume | ISSN | Citations |
88 | 0026-2714 | 2 |
PageRank | References | Authors |
0.40 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bahar Ahmadi | 1 | 3 | 0.79 |
| Bahram Javidi | 2 | 110 | 20.30 |
| Sina Shahbazmohamadi | 3 | 2 | 0.40 |