Name
Abstract | ||
|---|---|---|
Flip chip has been extensively used in microelectronic packaging industry. With the trend of solder bumps towards small volume and ultra-fine pitch, defect inspection of flip chips has become more difficult. In this work, we introduce radial basis function networks for detecting and predicting missing solder bumps, a typical defect in flip chips. Eight time and frequency domain features extracted from the flip chip vibration data are inputted to the networks, and flip chips with missing bumps distributed adjacently or randomly are detected and predicted. For the PAC2.1 flip chips with missing bumps distributed adjacently, we distinguish the defective flip chips from the reference one with a 100% accuracy. The flip chips with 1 to 2 missing solder bumps are then trained and detected accurately by the network, and the chip with 3 missing bumps can be predicted exactly as well. After that, we train the network with the data of flip chips with 1 to 4 missing bumps. The network can recognize the number of the missing bumps in these flip chips with a 100% accuracy, and predict 5 missing bumps in the flip chip with the accuracy of 91.7%. For further validation, we use the PB08 flip chips with missing bumps distributed adjacently or randomly for training, testing and prediction, and also obtain high accuracies. These prove the feasibility of using RBF networks for detection and prediction of flip chips with missing bumps in engineering. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.microrel.2015.09.030 | Microelectronics Reliability |
Keywords | Field | DocType |
Flip chip,Defect recognition,Prediction,Vibration analysis,RBF networks | Frequency domain,Flip chip,Microelectronics,Electronic engineering,Chip,Soldering,Vibration,Engineering | Journal |
Volume | Issue | ISSN |
55 | 12 | 0026-2714 |
Citations | PageRank | References |
1 | 0.36 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guanglan Liao | 1 | 36 | 9.69 |
| Li Du | 2 | 4 | 0.86 |
| Lei Su | 3 | 4 | 0.86 |
| Miao Zeng | 4 | 1 | 0.36 |
| Lei Nie | 5 | 62 | 15.24 |
| Tielin Shi | 6 | 90 | 17.20 |