Improvement of Sampling Inspection for Wire Bonding Using Thin AE Sensor and Transfer Learning
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- Koishi Yasutake
- Life Science and Systems Engineering, Kyushu Institute of Technology Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology
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- Ishida Shuichi
- Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology
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- Tabaru Tatsuo
- Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology
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- Iwasaki Wataru
- Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology
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- Miyamoto Hiroyuki
- Life Science and Systems Engineering, Kyushu Institute of Technology
Bibliographic Information
- Other Title
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- 薄型AEセンサと転移学習を用いたワイヤボンディング抜取検査の性能向上
- ウスガタ AE センサ ト テンイ ガクシュウ オ モチイタ ワイヤボンディング ヌキトリ ケンサ ノ セイノウ コウジョウ
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Description
<p>Wire bonding is an important process in semiconductor manufacturing. The quality of wire bonding is guaranteed by destructive sampling inspection. However, sampling inspection is not able to detect defective products that occur non-systematically. Therefore, it is desirable to shift to inspection of each part. In this paper, we propose an inspection method based on the wire bonding state using a Thin-film AE sensor and transfer learning. We place the thin film AE sensor in the immediate vicinity of the bonding junction and measure the applied AE wave. We improve the quality determination performance in the sampling inspection by determining the quality from the measured AE waves using the transfer learning method we developed. We applied the proposed method to experiment using actual bonding samples and confirmed that it showed a 10.7% higher quality determination ability than sampling inspection.</p>
Journal
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- Journal of The Japan Institute of Electronics Packaging
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Journal of The Japan Institute of Electronics Packaging 22 (3), 209-217, 2019-05-01
The Japan Institute of Electronics Packaging
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Details 詳細情報について
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- CRID
- 1390282763112916864
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- NII Article ID
- 130007644239
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- NII Book ID
- AA11231565
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- ISSN
- 1884121X
- 13439677
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- NDL BIB ID
- 029720184
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL Search
- Crossref
- CiNii Articles
- OpenAIRE
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- Abstract License Flag
- Disallowed