Machine learning-based virtual metrology on film thickness in amorphous carbon layer deposition process
Authors:
- Jeong Eun ChoiJCJeong Eun Choi
- Sang Jeen HongSang Jeen Hong
Open Access
- 1 August 2021
- journal article
- research article
- Published by Elsevier in Measurement: Sensors
- Vol. 16
Abstract
No abstract availableKeywords
Funding Information
- Ministry of Trade, Industry and Energy
- Korea Evaluation Institute of Industrial Technology (20006499)
This publication has 14 references indexed in Scilit:
- Virtual metrology for TSV etch depth measurement using optical emission spectroscopy2015
- In-situ virtual metrology for the silicon-dioxide etch rate by using optical emission spectroscopy dataJournal of the Korean Physical Society, 2014
- Optical In-Situ Plasma Process Monitoring Technique for Detection of Abnormal Plasma DischargeTransactions on Electrical and Electronic Materials, 2013
- A virtual metrology system for semiconductor manufacturingExpert Systems with Applications, 2009
- Use of neural network to model the deposition rate of PECVD-silicon nitride filmsPlasma Sources Science and Technology, 2005
- Neural network modeling of reactive ion etching using optical emission spectroscopy dataIEEE Transactions on Semiconductor Manufacturing, 2003
- Behavior of atomic radicals and their effects on organic low dielectric constant film etching in high density N2/H2 and N2/NH3 plasmasJournal of Applied Physics, 2002
- Integrated metrology: an enabler for advanced process control (APC)2001
- Importance of input data normalization for the application of neural networks to complex industrial problemsIEEE Transactions on Nuclear Science, 1997
- Characterization of a-C:H:N deposition from CH4/N2 rf plasmas using optical emission spectroscopyJournal of Applied Physics, 1996