Stress-free deposition of [001] preferentially oriented titanium thin film by Kaufman ion-beam source

2017 ◽  
Vol 638 ◽  
pp. 57-62 ◽  
Author(s):  
Imrich Gablech ◽  
Ondřej Caha ◽  
Vojtěch Svatoš ◽  
Jan Pekárek ◽  
Pavel Neužil ◽  
...  
Vacuum ◽  
1991 ◽  
Vol 42 (16) ◽  
pp. 1059
Author(s):  
David T Wei ◽  
Harold R Kaufman
Keyword(s):  
Ion Beam ◽  

2019 ◽  
Vol 670 ◽  
pp. 105-112 ◽  
Author(s):  
Imrich Gablech ◽  
Vojtěch Svatoš ◽  
Ondřej Caha ◽  
Adam Dubroka ◽  
Jan Pekárek ◽  
...  

Author(s):  
E E Suslov ◽  
A S Larionov ◽  
S B Kislitsin ◽  
I I Chernov ◽  
M S Staltsov ◽  
...  

2019 ◽  
Vol 682 ◽  
pp. 109-120 ◽  
Author(s):  
Wjatscheslaw Sakiew ◽  
Stefan Schrameyer ◽  
Marco Jupé ◽  
Philippe Schwerdtner ◽  
Nick Erhart ◽  
...  

2012 ◽  
Vol 1 (1) ◽  
pp. 46 ◽  
Author(s):  
Amir Mahyar Khorasani ◽  
Mohammad Reza Solymany yazdi ◽  
Mehdi Faraji ◽  
Alex Kootsookos

Thin-film coating plays a prominent role on the manufacture of many industrial devices. Coating can increase material performance due to the deposition process. Having adequate and precise model that can predict the hardness of PVD and CVD processes is so helpful for manufacturers and engineers to choose suitable parameters in order to obtain the best hardness and decreasing cost and time of industrial productions. This paper proposes the estimation of hardness of titanium thin-film layers as protective industrial tools by using multi-layer perceptron (MLP) neural network. Based on the experimental data that was obtained during the process of chemical vapor deposition (CVD) and physical vapor deposition (PVD), the modeling of the coating variables for predicting hardness of titanium thin-film layers, is performed. Then, the obtained results are experimentally verified and very accurate outcomes had been attained.


Author(s):  
Tamou Yoshitaka ◽  
Li Jian ◽  
Stephen W. Russell ◽  
James W. Mayer
Keyword(s):  

2000 ◽  
Vol 614 ◽  
Author(s):  
D.B. Fenner ◽  
J. Hautala ◽  
L.P. Allen ◽  
J.A. Greer ◽  
W.J. Skinner ◽  
...  

ABSTRACTThin-film magnetic sensor and memory devices in future generations may benefit from a processing tool for final-step etching and smoothing of surfaces to nearly an atomic scale. Gas-cluster ion-beam (GCIB) systems make possible improved surface sputtering and processing for many types of materials. We propose application of GCIB processing as a key smoothing step in thin-film magnetic-materials technology, especially spin-valve GMR. Results of argon GCIB etching and smoothing of surfaces of alumina, silicon, permalloy and tantalum films are reported. No accumulating roughness or damage is observed. The distinct scratches and tracks seen in atomic-force microscopy of CMP-processed surfaces, are removed almost entirely by subsequent GCIB processing. The technique primarily reduces high spatial-frequency roughness and renders the topographic surface elevations more nearly gaussian (randomly distributed).


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