Improved magnetoresistance through spacer thickness optimization in tilted pseudo spin valves based on L10 (111)-oriented FePtCu fixed layers

2009 ◽  
Vol 106 (5) ◽  
pp. 053909 ◽  
Author(s):  
C. L. Zha ◽  
Y. Y. Fang ◽  
J. Nogués ◽  
Johan Åkerman
Keyword(s):  
Spin Valves ◽  
Thickness Optimization ◽  
Spacer Thickness

Effects of spacer thickness on perpendicular anisotropy L10-FePt/TiN/L10-FePt pseudo spin valves

2012 ◽  
Vol 111 (8) ◽  
pp. 083909 ◽  
Author(s):  
P. Ho ◽  
G. C. Han ◽  
K. H. He ◽  
G. M. Chow ◽  
J. S. Chen
Keyword(s):  
Spin Valves ◽  
Perpendicular Anisotropy ◽  
Spacer Thickness

Pseudo spin-valves with different spacer thickness as sensing elements of mechanical strain

Vacuum ◽  
2012 ◽  
Vol 86 (6) ◽  
pp. 718-720 ◽  
Author(s):  
Š. Luby ◽  
B. Anwarzai ◽  
V. Áč ◽  
E. Majkova ◽  
R. Senderák
Keyword(s):  
Mechanical Strain ◽  
Spin Valves ◽  
Spacer Thickness

Interlayer Coupling and Magnetoresistance of MnIr-Based Spin Valves: Dependencies on Deposition Rate, Spacer Thickness, and Temperature

2007 ◽  
Vol 43 (7) ◽  
pp. 3143-3145 ◽  
Author(s):  
Jos Miguel Teixeira ◽  
Joo Oliveira Ventura ◽  
Rui Pedro Fermento ◽  
Joo Pedro Araujo ◽  
Joo Bessa Sousa ◽  
...  
Keyword(s):  
Deposition Rate ◽  
Interlayer Coupling ◽  
Spin Valves ◽  
Spacer Thickness

scholarly journals Thickness optimization of underlayer and seed layer for spin valves

2009 ◽  
Vol 65 (a1) ◽  
pp. s208-s208
Author(s):  
Hakan Cinar ◽  
R. Mustafa Oksuzoglu ◽  
Mustafa Yildirim
Keyword(s):  
Seed Layer ◽  
Spin Valves ◽  
Thickness Optimization

Spin transfer effects in current perpendicular to the plane spin valves

2005 ◽  
Vol 287 ◽  
pp. 325-332 ◽  
Author(s):  
M. Covington ◽  
M. AlHajDarwish ◽  
Y. Ding ◽  
A. Rebei ◽  
G.J. Parker ◽  
...  
Keyword(s):  
Spin Transfer ◽  
Spin Valves ◽  
Transfer Effects

Product Yield Enhancement by Spacer Thickness Variation Reduction

2021 ◽  
Author(s):  
Ritesh Ray Chaudhuri ◽  
William Simpson ◽  
Daniel Fedor ◽  
Prabhakar Bharatan ◽  
Joseph Thompson ◽  
...  
Keyword(s):  
Product Yield ◽  
Yield Enhancement ◽  
Thickness Variation ◽  
Variation Reduction ◽  
Spacer Thickness

scholarly journals An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 763
Author(s):  
Ran Yang ◽  
Zhenbo Wang ◽  
Jiajia Chen
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Food Product ◽  
Integrated Approach ◽  
Mechanistic Modeling ◽  
Bayesian Optimization ◽  
Initial Training ◽  
Thickness Optimization ◽  
Heating Uniformity ◽  
Food Design

Mechanistic-modeling has been a useful tool to help food scientists in understanding complicated microwave-food interactions, but it cannot be directly used by the food developers for food design due to its resource-intensive characteristic. This study developed and validated an integrated approach that coupled mechanistic-modeling and machine-learning to achieve efficient food product design (thickness optimization) with better heating uniformity. The mechanistic-modeling that incorporated electromagnetics and heat transfer was previously developed and validated extensively and was used directly in this study. A Bayesian optimization machine-learning algorithm was developed and integrated with the mechanistic-modeling. The integrated approach was validated by comparing the optimization performance with a parametric sweep approach, which is solely based on mechanistic-modeling. The results showed that the integrated approach had the capability and robustness to optimize the thickness of different-shape products using different initial training datasets with higher efficiency (45.9% to 62.1% improvement) than the parametric sweep approach. Three rectangular-shape trays with one optimized thickness (1.56 cm) and two non-optimized thicknesses (1.20 and 2.00 cm) were 3-D printed and used in microwave heating experiments, which confirmed the feasibility of the integrated approach in thickness optimization. The integrated approach can be further developed and extended as a platform to efficiently design complicated microwavable foods with multiple-parameter optimization.

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