Mobility spectrum approach in the analysis of the electrical conduction of a GaAs layer grown by molecular beam epitaxy

Regiński, K.; Marczewski, J.; Dziuba, Z.; Grodzicka, E.

doi:10.1063/1.366481

Cited by 4 publications

(3 citation statements)

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“…Further developments by several research groups led to what is known as mobility spectrum analysis (MSA), in which the generated spectra are optimized to quantitatively agree with the experimentally derived magnetic field-dependent Hall and resistivity results [2][3][4][5][6][7]. Recently, MSA has been utilized to study carrier transport in a variety of semiconductor nanostructures and devices [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Intrinsic broadening of the mobility spectrum of bulk n-type GaAs

et al. 2014

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Modern devices consisting of multiple semiconductor layers often result in the population of numerous distinct carrier species. Conventional Hall measurements at a single-magnetic-field strength provide only a weighted average of the electron mobility and carrier concentration of a semiconductor structure and, therefore, are of limited use for the extraction of carrier transport information. In recent years, mobility spectrum analysis techniques, which have been developed to extract a mobility spectrum from magnetic field-dependent conductivitytensor measurements, have been applied in the analysis of carrier conductivity mechanisms of numerous semiconductor structures and devices. Currently there is a severe lack of reported studies on theoretical calculations of the mobility distribution of semiconductor structures or devices. In addition, the majority of reports on experimental mobility spectrum analysis are of complex, multi layered structures such as type-II superlattices, and the interpretation of the mobility spectra has been difficult. Therefore, a good understanding of the mobility spectrum has yet to be developed. For example, it is often assumed that distinct peaks of a mobility spectrum result from fundamentally different conduction mechanisms such as the bulk and surface conduction of narrow-bandgap semiconductors. In this article, we present calculations of the electron mobility distribution of bulk GaAs, which predict the existence of multiple Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. mobility spectrum peaks that result from electron conductivity in the Γ conduction band. This report serves as an important and simple test case upon which experimentally measured mobility spectra can be compared. It also presents insight into the general nature of electron mobility distributions.

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Section: Introductionmentioning

confidence: 99%

Intrinsic broadening of the mobility spectrum of bulk n-type GaAs

et al. 2014

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“…However, performing the so-called inverse transform problem (that is, finding the mobility spectrum from magnetoresistance data) is non-trivial with a noisy, limited data set and is the subject of much investigation. [8,13,14,15,16,17,18,19] We replace the traditional MAXENT algorithm used by Kiatgamolchai et al [18,19] with Bryan's algorithm. [20] This approach has two major advantages over the previous technique: (1) A Bayesian analysis is performed on the resulting spectrum, and (2) the search process is optimized by null space decomposition of the kernel.…”

Section: Introductionmentioning

confidence: 99%

“…15 it is assumed that the mobility for a gas subjected to mixed scattering can be approximated by the phenomenological expression:…”

Section: Introductionmentioning

confidence: 99%

Application of Bryan’s algorithm to the mobility spectrum analysis of semiconductor devices

Chrastina

Hague

Leadley

2003

Journal of Applied Physics

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A powerful method for mobility spectrum analysis is presented, based on Bryan's maximum entropy algorithm. The Bayesian analysis central to Bryan's algorithm ensures that we avoid overfitting of data, resulting in a physically reasonable solution. The algorithm is fast, and allows the analysis of large quantities of data, removing the bias of data selection inherent in all previous techniques. Existing mobility spectrum analysis systems are reviewed, and the performance of the Bryan's Algorithm Mobility Spectrum (BAMS) approach is demonstrated using synthetic data sets. Analysis of experimental data is briefly discussed. We find that BAMS performs well compared to existing mobility spectrum methods.

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