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| Content Provider | Springer Nature Link |
|---|---|
| Author | Kim, Hyung Tae Lee, Kang Won Yang, Hae Jeong Kim, Sung Chul |
| Copyright Year | 2013 |
| Abstract | We propose a self-learning method for automatic wafer alignment in the semiconductor manufacturing process. A feed forward neural network is trained by and used for wafer alignment. The network determines the movement of kinematic parts from the misalignment inspected by machine vision. However, it is time-consuming and inconvenient to obtain training data in this way. So, we built an automatic learning rule to gather the data and train the network. The network may determine wrong outputs and cause other misalignments at first, but the error can decrease as the training proceeds. The training sets consisted of a variation of misalignment data and the movement of an alignment stage. Five recent sets are used for training and others are dismissed or forgotten. This retrained network tried aligning, measured misalignment, and made new training sets. This sequence makes it possible to acquire alignment skill and automate the process. After learning, automatic alignment accomplished sub-pixel accuracy for several cases of misalignment. The result showed that the proposed method could be applied to the semiconductor manufacturing process. Its performance improved about 6% compared with conventional algorithms. |
| Starting Page | 215 |
| Ending Page | 221 |
| Page Count | 7 |
| File Format | |
| ISSN | 22347593 |
| Journal | International Journal of Precision Engineering and Manufacturing |
| Volume Number | 14 |
| Issue Number | 2 |
| e-ISSN | 20054602 |
| Language | English |
| Publisher | Korean Society for Precision Engineering |
| Publisher Date | 2013-01-23 |
| Publisher Place | Springer |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Self-Training Iterative Learning Control Machine Intelligence Automatic Alignment Wafer Processing Industrial and Production Engineering Materials Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Industrial and Manufacturing Engineering Mechanical Engineering Electrical and Electronic Engineering |
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