scholarly journals Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon

2019 ◽  
Vol 10 ◽  
pp. 100140 ◽  
Author(s):  
X. Qian ◽  
S. Peng ◽  
X. Li ◽  
Y. Wei ◽  
R. Yang
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Amorphous Silicon ◽  
Conductivity Modeling

Machine learning reveals the complexity of dense amorphous silicon

Nature ◽  
2021 ◽  
Vol 589 (7840) ◽  
pp. 22-23
Author(s):  
Paul F. McMillan
Keyword(s):  
Machine Learning ◽  
Amorphous Silicon

scholarly journals Hydrogen effects on the thermal conductivity of delocalized vibrational modes in amorphous silicon nitride (a−SiNx:H)

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Jeffrey L. Braun ◽  
Sean W. King ◽  
Eric R. Hoglund ◽  
Mehrdad Abbasi Gharacheh ◽  
Ethan A. Scott ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Silicon Nitride ◽  
Amorphous Silicon ◽  
Vibrational Modes ◽  
Amorphous Silicon Nitride

Determination of Thermal Conductivity of Amorphous Silicon Thin Films via Non-Contacting Optical Probing

2006 ◽  
Vol 326-328 ◽  
pp. 689-692
Author(s):  
Seung Jae Moon
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Thermal Conductivity ◽  
Amorphous Silicon ◽  
Optical Transmission ◽  
Film Deposition ◽  
Transmission Measurement ◽  
Conductive Heat ◽  
Optical Transmission Measurement

The thermal conductivity of amorphous silicon (a-Si) thin films is determined by using the non-intrusive, in-situ optical transmission measurement. The thermal conductivity of a-Si is a key parameter in understanding the mechanism of the recrystallization of polysilicon (p-Si) during the laser annealing process to fabricate the thin film transistors with uniform characteristics which are used as switches in the active matrix liquid crystal displays. Since it is well known that the physical properties are dependent on the process parameters of the thin film deposition process, the thermal conductivity should be measured. The temperature dependence of the film complex refractive index is determined by spectroscopic ellipsometry. A nanosecond KrF excimer laser at the wavelength of 248 nm is used to raise the temperature of the thin films without melting of the thin film. In-situ transmission signal is obtained during the heating process. The acquired transmission signal is fitted with predictions obtained by coupling conductive heat transfer with multi-layer thin film optics in the optical transmission measurement.

Thermal conductivity modeling of circular-wire nanocomposites

2010 ◽  
Vol 108 (4) ◽  
pp. 044306 ◽  
Author(s):  
Tse-Yang Hsieh ◽  
Jaw-Yen Yang
Keyword(s):  
Thermal Conductivity ◽  
Conductivity Modeling

scholarly journals Thermal Conductivity Modeling of Aqueous CuO Nanofluids by Adaptive Neuro-Fuzzy Inference System (ANFIS) Using Experimental Data

2017 ◽  
Vol 62 (2) ◽  
pp. 202 ◽  
Author(s):  
Mohammad Hemmat Esfe
Keyword(s):  
Thermal Conductivity ◽  
Input Data ◽  
Fuzzy Inference ◽  
Regression Coefficient ◽  
Good Precision ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Triangular Function ◽  
Best Responses ◽  
Conductivity Modeling

In this article, thermal conductivity data of aqueous nanofluids of CuO have been modeled through one of the instruments of empirical data modeling. The input data of 5 different volume fractions of nanofluid obtained in four temperatures through experiments have been considered as network inputs. Also, triangular function, due to providing the best responses, has been used as membership function in ANFIS structure. The modeling results show that fuzzy networks are able to model thermal conductivity results of nanofluids with good precision. Regression coefficient of this modeling has been 0.99.

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