Spatially resolved electronic structure of an isovalent nitrogen center in GaAs

Plantenga, RC; Kortan, VR; Kaizu, Toshiyuki; Harada, Yukihiro; Flatté, Michael E.; Koenraad, PM Paul

doi:10.1103/physrevb.96.155210

Cited by 11 publications

(18 citation statements)

References 74 publications

(186 reference statements)

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“…Information about confined states in QDs can be obtained by scanning tunneling spectroscopy 56 – 58 . Furthermore, X-STM can also resolve the effects of isoelectronic impurities in semiconductors (such as GaAs and InP) with atomic resolution 59 – 64 . Atom probe tomography (APT) has the potential to obtain the complete three-dimensional reconstruction of the topography along with mass spectral analysis, identifying, thus, different chemical species.…”

Section: Introductionmentioning

confidence: 99%

Structural and compositional analysis of (InGa)(AsSb)/GaAs/GaP Stranski–Krastanov quantum dots

et al. 2021

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We investigated metal-organic vapor phase epitaxy grown (InGa)(AsSb)/GaAs/GaP Stranski–Krastanov quantum dots (QDs) with potential applications in QD-Flash memories by cross-sectional scanning tunneling microscopy (X-STM) and atom probe tomography (APT). The combination of X-STM and APT is a very powerful approach to study semiconductor heterostructures with atomic resolution, which provides detailed structural and compositional information on the system. The rather small QDs are found to be of truncated pyramid shape with a very small top facet and occur in our sample with a very high density of ∼4 × 1011 cm−2. APT experiments revealed that the QDs are GaAs rich with smaller amounts of In and Sb. Finite element (FE) simulations are performed using structural data from X-STM to calculate the lattice constant and the outward relaxation of the cleaved surface. The composition of the QDs is estimated by combining the results from X-STM and the FE simulations, yielding ∼InxGa1 − xAs1 − ySby, where x = 0.25–0.30 and y = 0.10–0.15. Noticeably, the reported composition is in good agreement with the experimental results obtained by APT, previous optical, electrical, and theoretical analysis carried out on this material system. This confirms that the InGaSb and GaAs layers involved in the QD formation have strongly intermixed. A detailed analysis of the QD capping layer shows the segregation of Sb and In from the QD layer, where both APT and X-STM show that the Sb mainly resides outside the QDs proving that Sb has mainly acted as a surfactant during the dot formation. Our structural and compositional analysis provides a valuable insight into this novel QD system and a path for further growth optimization to improve the storage time of the QD-Flash memory devices.

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Section: Introductionmentioning

confidence: 99%

Structural and compositional analysis of (InGa)(AsSb)/GaAs/GaP Stranski–Krastanov quantum dots

et al. 2021

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“…Earlier X-STM studies on N-doped GaAs provide a complete description of the properties of isolated undecorated N atoms in or near the (110) surface of GaAs [19][20][21][22]. Under filled state (negative voltage) imaging conditions N atoms give rise to a depression of the (110) surface, with the size and depth of the depression indicating how far below the surface plane the N atom is located.…”

Section: Resultsmentioning

confidence: 99%

“…Under filled state (negative voltage) imaging conditions N atoms give rise to a depression of the (110) surface, with the size and depth of the depression indicating how far below the surface plane the N atom is located. In empty state imaging (positive voltage) N atoms show an anisotropic bright structure which is highly dependent on the tunneling voltage and the distance the atom resides from the (110) cleaved surface [20,28]. This information provides us with a way to determine the exact position of the N atoms with respect to the surface in the measurements.…”

Section: Resultsmentioning

confidence: 99%

“…Using this anisotropy and comparing our measurements with Refs. [20,28] we were able to determine the exact lattice orientation of the surface.…”

Section: A B1/b2 and C1/c2 Featuresmentioning

confidence: 99%

“…N atoms in N-doped GaAs have already been successfully imaged with the use of cross-sectional scanning tunneling microscopy (X-STM) [19][20][21][22]. Here we present the X-STM work on hydrogenated N-doped GaAs where we observe features related to the hydrogenation of N-doped GaAs.…”

Section: Introductionmentioning

confidence: 98%

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N−nH complexes in GaAs studied at the atomic scale by cross-sectional scanning tunneling microscopy

et al. 2020

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Hydrogenation of nitrogen (N) doped GaAs allows for reversible tuning of the band gap and the creation of site controlled quantum dots through the manipulation of N-nH complexes, N-nH complexes, wherein a nitrogen atom is surrounded by n hydrogen (H) atoms. Here we employ cross-sectional scanning tunneling microscopy (X-STM) to study these complexes in the GaAs (110) surface at the atomic scale. In addition to that we performed density functional theory (DFT) calculations to determine the atomic properties of the N-nH complexes. We argue that at or near the (110) GaAs surface two H atoms from N-nH complexes dissociate as an H 2 molecule. We observe multiple features related to the hydrogenation process, of which a subset is classified as N-1H complexes. These N-1H related features show an apparent reduction of the local density of states (LDOS), characteristic to N atoms in the GaAs (110) surface with an additional apparent localized enhancement of the LDOS located in one of three crystal directions. N-nH features can be manipulated with the STM tip. Showing in one case a switching behavior between two mirror-symmetric states and in another case a removal of the localized enhancement of the LDOS. The disappearance of the bright contrast is most likely a signature of the removal of an H atom from the N-nH complex.

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Framework for atomic-level characterisation of quantum computer arrays by machine learning

et al. 2020

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Atomic level qubits in silicon are attractive candidates for large-scale quantum computing, however, their quantum properties and controllability are sensitive to details such as the number of donor atoms comprising a qubit and their precise location. This work combines machine learning techniques with million-atom simulations of scanning-tunnelling-microscope (STM) images of dopants to formulate a theoretical framework capable of determining the number of dopants at a particular qubit location and their positions with exact lattice-site precision. A convolutional neural network was trained on 100,000 simulated STM images, acquiring a characterisation fidelity (number and absolute donor positions) of above 98% over a set of 17,600 test images including planar and blurring noise. The method established here will enable a high-precision post-fabrication characterisation of dopant qubits in silicon, with highthroughput potentially alleviating the requirements on the level of resource required for quantum-based characterisation, which may be otherwise a challenge in the context of large qubit arrays for universal quantum computing.Of the leading platforms for the implementation of quantum computing architectures, qubits based on the spin of individual dopant atoms in silicon 1-7 are growing in interest given the nexus with nanoelectronics engineering and the long coherence times 8,9 . For the exchange-based quantum computer design proposals 1,2,10 where the physical separations between atomic qubits are small (10-15 nm), the pathway for scale-up to large two-dimensional arrays generally relies on uniformity of control of qubits and their interactions. Even small variations at the level of one lattice-site for qubits based on single or multiple dopant atoms can significantly affect the design and control of logical operations. While the details of few qubit systems can be determined using electrostatics and electron spin resonance 11 and variations in interactrions mitigated by designing appropriate pulse schemes 12,13 , for large-scale arrays a reliable and fast method of identification (atom count per qubit) and characterisation (exact spatial location of atoms in lattice) is critical. Machine intelligence techniques have been extremely productive in a wide range of applications including material design, medical imaging, and data science, where the design space is enormously large [14][15][16][17] and/or autonomous predictions are required from big data analysis 18 . In quantum devices, the application of deep learning for the automated fabrication of atomic-scale surface defects has been proposed 19,20 . This work integrates the high efficiency of machine learning algorithms towards pattern recognition 21 with multi-million-atom simulations of Scanning-tunnelling microscope (STM) images of donor wave functions 22,23 to formulate a theoretical framework with the capability of high-throughput and automated spatial metrology of the donor qubits in silicon. The ability to pinpoint the donor locations with exact-atom p...

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Spatially resolved electronic structure of an isovalent nitrogen center in GaAs

Cited by 11 publications

References 74 publications

Structural and compositional analysis of (InGa)(AsSb)/GaAs/GaP Stranski–Krastanov quantum dots

Structural and compositional analysis of (InGa)(AsSb)/GaAs/GaP Stranski–Krastanov quantum dots

N−nH complexes in GaAs studied at the atomic scale by cross-sectional scanning tunneling microscopy

Framework for atomic-level characterisation of quantum computer arrays by machine learning

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