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Record W2912015325 · doi:10.1149/ma2018-02/31/1083

(Invited) Isotopically Programmed Group IV Semiconductors: A Versatile Platform for Quantum Technologies

2018· article· en· W2912015325 on OpenAlexaff
Oussama Moutanabbir, Samik Mukherjee

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsIsotopeSemiconductorNeutronChemistryPhysicsMaterials scienceNanotechnologyChemical physicsNuclear physicsOptoelectronics

Abstract

fetched live from OpenAlex

The introduction of stable isotopes as an additional degree of freedom in the growth of semiconductor films and quantum structures provides a wealth of opportunities to manipulate their basic properties, design an entirely new class of devices, and highlight subtle but important nanoscale and quantum phenomena. In this presentation, I will describe the historical context and outline the recent progress in the area of isotopically engineering group IV semiconductors with focus on nanoscale and quantum structures and devices. This ability to isotopically program semiconductor structures has been a powerful paradigm to investigate and manipulate some of the important physical properties of semiconductors and exploit them in innovative device structures [1-13]. Isotopes of an element differ in the number of neutrons in the nucleus. This creates differences between the isotopes in their lattice dynamics and nuclear properties. For instance, the slight difference in zero-point motion leads to a difference in atomic volume between the isotope atoms, which influences the lattice constant [3]. Also, the difference in electron-phonon coupling between crystals of different isotopic composition was found to affect the electronic band gap [4]. The nuclear spin is another significant difference between stable isotopes. For instance, natural silicon (Si) has three stable isotopes: 28 Si, 29 Si, and 30 Si, with isotopic abundances of 92.23%, 4.67%, and 3.10%, respectively. Among these three isotopes, only 29 Si has a nuclear spin of ½, whereas 28 Si and 30 Si are nuclear spin-free. This property has been crucial in the realization of Si-based quantum information devices [5-8]. One of the most drastic isotope related effect in semiconductors is found in phonon properties [9-13]. Mass fluctuation induced by isotope disorder acts as a substitutional defect in a crystal thus affecting the phonon mean free path and consequently the phononic thermal conductivity. Measurements on isotopically pure Ge [9] and Si [10] crystals showed an enhanced thermal conductivity as compared to their natural counterparts. Also, lower thermal conductivity was recently demonstrated in Si isotope superlattices [11]. All the properties of semiconductor stable isotopes have been investigated and exploited in bulk materials or thin films. Herein, we will describe the new opportunities emerging from the combinations of the isotope effects with size-related effects in nanoscale materials [14-18]. More specifically, we will discuss phonon engineering in metal catalyzed silicon nanowires with tailor-made isotopic compositions grown using isotopically enriched silane and german precursors 28 SiH 4 , 29 SiH 4 , 30 SiH 4 , 74 GeH 4 and 76 GeH 4 , with purity better than 99.9%. Isotopically mixed nanowires 28 Si x 30 Si 1-x with a composition close to the highest mass disorder ( x ~ 0.5) were used as a playground to elucidate the interplay between nanoscale interface phenomena and heat transport [16]. We will show how isotopically engineered nanowire homo-junctions can be introduced to realize innovative phononic devices such as thermal diodes and thermal transistors. Additionally, we will also discuss the use of nuclear spin-full 29 Si to engineer novel quantum devices in nuclear spin-free SiGe nanostructures. Finally, atomistic-level investigations of isotopically programmed nanoscale materials will be presented based on laser-assisted atom probe tomography [15,17]. References [1] M. Cardona et al., Rev. Moden Phys. 77, 1173 (2005). [2] E. E. Haller, MRS Bull. 31, 547 (2006). [3] M. Hu et al., M. Phys. Rev. B 67, 113306 (2003). [4] G. Davis et al., Semicond. Sci. Technol. 7, 1271 (1992). [5] A. M. Tyryshkin et al., Nat. Mater. 11, 143 (2012). [6] D. R. McCamey et al., Science 330, 1652 (2010). [7] S. Simmons et al., Nature 470, 69 (2011). [8] K. M. Itoh, Solid State Commun. 133, 747 (2005). [9] V. I. Ozhogin et al., J. Exp. Theor. Phys. Lett. 63, 490 (1996). [10] R. K. Kremer et al., J. Solid State Commun. 131, 499 (2004). [11] H. Bracht et al., New J. Phys. 16, 015021 (2014). [12] M. Nakajima et al., Phys. Rev. B 63, 161304 (2001). [13] D. Morelli et al., Phys. Rev. B 66, 195304 (2002). [14] O. Moutanabbir et al., Phys. Rev. Lett. 105, 026101 (2010) [15] O. Moutanabbir et al., Appl. Phys. Lett. 98, 013111 (2011). [16] S. Mukherjee et al., Nano Letters 15, 3885 (2015). [17] S. Mukherjee et al., Nano Letters 16, 1335 (2016). [18] S. Mukherjee et al., Nano Letters, under review (2018).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.246
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2018
Admission routes1
Has abstractyes

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