Prediction of Wafer Performance: Use of Functional Outlier Detection and Regression
Optical emission spectroscopy (OES) data is essential for virtual metrology, enabling accurate predictions of wafer performance in plasma etching processes. This approach not only reduces the need for physical measurements of product quality, leading to significant resource savings, but also support...
Saved in:
| Main Authors: | Kyusoon Kim, Seunghee Oh, Kiwook Bae, Hee-Seok Oh |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10898005/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Lavender hydrosol analysis using UV spectroscopy data and partial least squares regression
by: Sára Preiner, et al.
Published: (2025-06-01) -
A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement
by: Thafer Ramathan Muttar AL-Badrany, et al.
Published: (2023-02-01) -
Estimating Outliers Using the Iterative Method in Partial Least Squares Regression Analysis for Linear Models.
by: Mahammad Bazid, et al.
Published: (2025-06-01) -
Rapid discrimination of soil variety based on spectroscopic techniques
by: WANG Zun-yi, et al.
Published: (2010-05-01) -
Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of <i>Tradescantia</i> Plants
by: Renan Falcioni, et al.
Published: (2024-10-01)