Numerical backstepping for diameter control of silicon ingots in the Czochralski process

2012 ◽  
Author(s):  
Parsa Rahmanpour ◽  
Morten Hovd
Keyword(s):  
Diameter Control ◽  
Czochralski Process

In-process force measurement for diameter control in precision cylindrical grinding

2008 ◽  
Vol 42 (1-2) ◽  
pp. 93-101 ◽  
Author(s):  
Alex W. Moerlein ◽  
Eric R. Marsh ◽  
Theodore R. S. Deakyne ◽  
R. Ryan Vallance
Keyword(s):  
Force Measurement ◽  
Cylindrical Grinding ◽  
Diameter Control

Diameter control of single-walled carbon nanotubes using argon–helium mixture gases

2001 ◽  
Vol 115 (14) ◽  
pp. 6752-6759 ◽  
Author(s):  
Samir Farhat ◽  
Marc Lamy de La Chapelle ◽  
Annick Loiseau ◽  
Carl D. Scott ◽  
Serge Lefrant ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Single Walled Carbon Nanotubes ◽  
Single Walled Carbon ◽  
Helium Mixture ◽  
Diameter Control ◽  
Walled Carbon Nanotubes

Enhanced Automatic Diameter Control System of Czochralski-grown Crystals

1996 ◽  
Vol 31 (6) ◽  
pp. 789-793 ◽  
Author(s):  
V. V. Kochurikhin ◽  
K. Shimamura ◽  
T. Fukuda
Keyword(s):  
Control System ◽  
Diameter Control

scholarly journals Fabrication methods and high-precision diameter control techniques of optical micro-/nanofibers

2020 ◽  
Vol 50 (8) ◽  
pp. 084212
Author(s):  
Yi KANG ◽  
Lei ZHANG ◽  
Yu XIE ◽  
LiMin TONG ◽  
Wei FANG ◽  
...  
Keyword(s):  
High Precision ◽  
Control Techniques ◽  
Diameter Control

scholarly journals Advanced machine learning decision policies for diameter control of carbon nanotubes

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Rahul Rao ◽  
Jennifer Carpena-Núñez ◽  
Pavel Nikolaev ◽  
Michael A. Susner ◽  
Kristofer G. Reyes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carbon Nanotubes ◽  
Radial Breathing Mode ◽  
Expected Improvement ◽  
Synthesis Conditions ◽  
Relative Closeness ◽  
Diameter Control ◽  
Decision Policies ◽  
Walled Carbon Nanotubes ◽  
Growth Experiments

AbstractThe diameters of single-walled carbon nanotubes (SWCNTs) are directly related to their electronic properties, making diameter control highly desirable for a number of applications. Here we utilized a machine learning planner based on the Expected Improvement decision policy that mapped regions where growth was feasible vs. not feasible and further optimized synthesis conditions to selectively grow SWCNTs within a narrow diameter range. We maximized two ranges corresponding to Raman radial breathing mode frequencies around 265 and 225 cm−1 (SWCNT diameters around 0.92 and 1.06 nm, respectively), and our planner found optimal synthesis conditions within a hundred experiments. Extensive post-growth characterization showed high selectivity in the optimized growth experiments compared to the unoptimized growth experiments. Remarkably, our planner revealed significantly different synthesis conditions for maximizing the two diameter ranges in spite of their relative closeness. Our study shows the promise for machine learning-driven diameter optimization and paves the way towards chirality-controlled SWCNT growth.

Gray-box model-based predictive control of Czochralski process

2021 ◽  
Vol 573 ◽  
pp. 126299
Author(s):  
Shota Kato ◽  
Sanghong Kim ◽  
Masahiko Mizuta ◽  
Masanori Oshima ◽  
Manabu Kano
Keyword(s):  
Predictive Control ◽  
Box Model ◽  
Model Based ◽  
Czochralski Process
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