Name
Abstract | ||
|---|---|---|
The heterodyne laser interferometer acts as an ultra-precise measurement apparatus in semiconductor manufacture. However the periodical nonlinearity property caused from frequency cross-talk is an obstacle to improve the high measurement accuracy in nanometer scale. In order to minimize the nonlinearity error of the heterodyne interferometer, we propose a frequency cross-talk compensation algorithm using an artificial intelligence method. The feedforward neural network trained by back-propagation compensates the nonlinearity error and regulates to minimize the difference with the reference signal. With some experimental results, the improved accuracy is proved through comparison with the position value from a capacitive displacement sensor. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1587/transfun.E92.A.681 | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES |
Keywords | Field | DocType |
laser interferometry, nonlinearity compensation, frequency cross-talk, neural network | Feedforward neural network,Nonlinear system,Laser,Theoretical computer science,Capacitive displacement sensor,Interferometry,Heterodyne,Accuracy and precision,Acoustics,Artificial neural network,Electrical engineering,Mathematics | Journal |
Volume | Issue | ISSN |
E92A | 2 | 0916-8508 |
Citations | PageRank | References |
1 | 0.48 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wooram Lee | 1 | 10 | 3.37 |
| Gunhaeng Heo | 2 | 2 | 1.45 |
| Kwan-Ho You | 3 | 11 | 4.20 |