Hall mobility and electron trap density in GaAsN grown by liquid phase epitaxy

Dhar, S.; Mondal, A.; Das, T.D.

doi:10.1088/0268-1242/23/1/015007

Cited by 8 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where e is the electron charge, µ e = 300 cm 2 Vs −1 , and µ hh = 50 cm 2 Vs −1 [81][82][83][84][85][86] are the electron, light hole and heavy hole mobilities. In contrast to equation ( 2), the exact Drude expression for the longitudinal conductivity contain magnetic field-dependent terms.…”

Section: Photoluminescence and Pcmentioning

confidence: 99%

Machine learning assisted GaAsN circular polarimeter

Aguirre-Perez¹,

Joshya²,

Carrère³

et al. 2022

J. Opt.

View full text Add to dashboard Cite

We demonstrate the application of a two stage machine learning algorithm that enables to correlate the electrical signals from a GaAsxN1-x circular polarimeter with the intensity, degree of circular polarization and handedness of an incident light beam. Specifically, we employ a multimodal logistic regression to discriminate the handedness of light and a 6-layer neural network to establish the relationship between the input voltages, the intensity and degree of circular polarization. We have developed a particular neural network training strategy that substantially improves the accuracy of the device. The algorithm was trained and tested on theoretically generated photoconductivity and on photoluminescence experimental results. Even for a small training experimental dataset (70 instances), it is shown that the proposed algorithm correctly predicts linear, right and left circularly polarized light misclassifying less than 1.5% of the cases and attains an accuracy larger than 97% in the vast majority of the predictions (92%) for intensity and degree of circular polarization. These numbers are significantly improved for the larger theoretically generated datasets (4851 instances). The algorithm is versatile enough that it can be easily adjusted to other device configurations where a map needs to be established between the input parameters and the device response. Training and testing data files as well as the algorithm are provided as supplementary material.

show abstract

Section: Photoluminescence and Pcmentioning

confidence: 99%

Machine learning assisted GaAsN circular polarimeter

Aguirre-Perez¹,

Joshya²,

Carrère³

et al. 2022

J. Opt.

View full text Add to dashboard Cite

show abstract

“…Despite years of extensive studies only a very limited effort has been directed towards understanding of the transport properties of the GaNAs alloy system [8][9][10][11][12][13][14][15]. It has been shown that mobility in the In y Ga 1-y N x As 1-x is limited mainly by two scattering mechanisms: the band broadening scattering (BBS) and the random field scattering (RFS) [3].…”

mentioning

confidence: 99%

Hall mobilities in GaN_xAs_1‐x

Olea

Walukiewicz

et al. 2010

Phys. Status Solidi (c)

View full text Add to dashboard Cite

In this work we report a systematic study of the electron and hole mobilities of GaNxAs1‐x alloys with different dopants (Zn, Te) and carrier concentrations (1017‐1019 cm‐3). We found a very slight reduction of the hole mobility in p‐GaNxAs1‐x compared to p‐GaAs, indicating that for small N contents (∼1.6%) the valence band is not affected by the N incorporation. In a striking contrast, incorporation of even small amounts of N leads to an abrupt reduction of the electron mobility in n‐GaNxAs1‐x. We further show that the processes that limit the mobility in GaNxAs1‐x can be explained by the band broadening and the random field scatterings. Considering these two scattering mechanisms we calculated the dependence of electron mobilities on electron concentration as well as on N composition in GaNxAs1‐x. The calculations agree reasonably well with experiment data of maximum electron mobilities with alloy composition. (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

show abstract