Machine learning based model for plasma prediction in high power pulsed magnetron sputtering processes
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Metal and Thin Film Mechanics · Plasma Diagnostics and Applications · Advanced Sensor Technologies Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (15)
Show source-provided references
1. Effect of peak power in reactive high power impulse magnetron sputtering of titanium dioxide10.1016/j.surfcoat.2011.04.071 ↗
2. Influence of high power densities on the composition of pulsed magnetron plasmas10.1016/S0042-207X(01)00475-4 ↗
3. Influence of dcMS and HPPMS in a dcMS/HPPMS hybrid process on plasma and coating properties10.1016/j.tsf.2016.07.079 ↗
4. Narx Neural Networks Models for Prediction of Standardized Precipitation Index in Central Mexico10.3390/atmos13081254 ↗
5. Real-time monitoring of plasma synthesis of functional materials by high power impulse magnetron sputtering and other PVD processes: towards a physics-constrained digital twin10.1088/1361-6463/aca25a ↗
6. Use of neural network to model the deposition rate of PECVD-silicon nitride films10.1088/0963-0252/14/1/011 ↗
7. CVD and PVD coating process modelling by using artificial neural networks10.5430/air.v1n1p46 ↗
8. Künstliche neuronale Netze sparen Iterationsschritte in der PVD-Technologie
9. Multilayer nitride coating performance optimized by an artificial neural network approach10.1016/j.ctmat.2015.10.003 ↗
10. Synthesis and tribological investigation of PVD hard coatings deposited via hybrid dcMS/HPPMS for application in plastics processing10.1063/1.5016716 ↗
11. Understanding the deformation and cracking behavior of Cr-based coatings deposited by hybrid direct current and high power pulse magnetron sputtering: From nitrides to oxynitrides10.1016/j.tsf.2019.06.004 ↗
12. Rasmussen · 2006
13. State-of-the-art in artificial neural network applications: A survey10.1016/j.heliyon.2018.e00938 ↗
14. Training feed-forward networks with the Marquardt algorithm10.1109/72.329697 ↗
15. K. L. Priddy, P.E. Keller, Artificial neural networks: An Introduction, SPIE, Bellingham, Wash. (1000 20th St. Bellingham WA 98225-6705 USA), (2005), ISBN: 9780819478726.10.1117/3.633187 ↗