Investigations of electrical and thermal properties in semiconductor device based on a thermoelectrical model

Chen, Jia; Zhang, Xiaobing

doi:10.1007/s10853-018-3014-9

Cited by 11 publications

(2 citation statements)

References 36 publications

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“…Combing the oscilloscope and high-speed camera analysis, the heating process of SCBs is divided into four steps: warming, melting, gasification, and ionization [ 6 , 7 ]. In addition, the heat transfer model of SCBs is established under the assumption that SCBs have a monolithic heating source [ 8 , 9 ]. However, these models cannot reveal the actual heating dynamics of the SCB since the microstructure of the SCB is not considered.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Heating Dynamics of a Semiconductor Bridge Initiator with Deep Neural Network

Tan

et al. 2022

Micromachines

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A semiconductor bridge (SCB) is an ignition device that provides a safe and efficient method widely used in civilian and military fields. The heating process of an SCB under electrical stimulation has a wide range of applications owing to its unique energy release process. However, the temperature variation of an SCB is challenging to obtain, both experimentally because of the rapid reaction on a microscale and with simulation due to its high demand in nonlinear calculations. In this study, we propose deep learning (DL) approach to study the electrothermal-coupled multi-physical heating process of the SCB initiator. We generated training data with multi-physics simulation (MPS), producing surface temperature distributions of SCBs under different voltages. The model was then trained with partial data in this database and evaluated on a separate test set. A generative adversarial network (GAN) with a customized loss function was used for modeling point-wise temperature dynamics. In the test set, our proposed method can predict the temperature distribution of an SCB under different voltages with high accuracy of over 0.9 during the heating process. We reduced the computation time by several orders of magnitude by replacing MPS with a deep neural network.

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Section: Introductionmentioning

confidence: 99%

Modeling the Heating Dynamics of a Semiconductor Bridge Initiator with Deep Neural Network

Tan

et al. 2022

Micromachines

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“…For the properties of these electrical devices are highly dependent on the operating temperature, the comprehensive understanding of the thermal conductivity and the Journal of Physics: Condensed Matter Nanoscale size effect and phonon properties of silicon material through simple spectral energy density analysis based on molecular dynamics spectral phonon properties is of critical importance. When the thickness of the device and the phonon mean free path (MFP) are in the same order of magnitude, it is a real challenge to predict the thermal transport in such systems [1][2][3][4]. In such structures the phonons are the dominant carriers of thermal energy transport, hence it is essential to get a thorough understanding of the spectral phonon properties which will determine the successful management of the heat transfer in these thermoelectric materials [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Nanoscale size effect and phonon properties of silicon material through simple spectral energy density analysis based on molecular dynamics

Chen

Zhang

2019

J. Phys.: Condens. Matter

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Due to the importance of spectral analysis for the development of highly efficient semiconductor silicon devices and the complexity of current research techniques, the realization of an effective and simple method for phonon spectral analysis is imperative. Based on the molecular dynamics (MD) simulations and the phonon spectral energy density (SED) analysis (Thomas et al 2010 Phys. Rev. B 81 081411), a straightforward method is adopted to obtain the phonon dispersion for silicon films. The MD method is used to investigate the heat conduction of the three-dimensional (3D) thin silicon film with Stillinger–Weber (SW) potential. The thermal conductivity of the silicon is obtained from the non-equilibrium molecular dynamics (NEMD) simulation by using Muller–Plathe (M–P) method (Müller-Plathe 1997 J. Chem. Phys. 106 6082). For further analysis of thermal transport properties based on the phonon concept, the SED analysis technique is utilized by adopting the previously obtained atomic velocities as input. Moreover, the nanoscale size effect on the spectral analysis is considered. Domains with different sizes are studied to achieve a sufficient resolution of the dispersion relation from the phonon SED. The comparison with the results of existing approach demonstrates that the utilized method can accurately and directly obtain the phonon SED profiles along the frequency axis.

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Floor-Mounted Heating of Piglets with the Use of Thermoelectricity

Tikhomirov

Trunov

Kuzmichev

et al. 2021

Advances in Intelligent Systems and Computing

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Investigations of electrical and thermal properties in semiconductor device based on a thermoelectrical model

Cited by 11 publications

References 36 publications

Modeling the Heating Dynamics of a Semiconductor Bridge Initiator with Deep Neural Network

Modeling the Heating Dynamics of a Semiconductor Bridge Initiator with Deep Neural Network

Nanoscale size effect and phonon properties of silicon material through simple spectral energy density analysis based on molecular dynamics

Floor-Mounted Heating of Piglets with the Use of Thermoelectricity

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