Energy and matter-efficient size-selective growth of thin quantum wires in a plasma

It is shown that plasmas can minimize the adverse Gibbs-Thompson effect in thin quantum wire growth. The model of Si nanowirenucleation includes the unprecedented combination of the plasma sheath, ion- and radical-induced species creation and heating effects on the surface and within an Au catalyst nanoparticle. Compared to neutral gas thermal processes, much thinner, size-selective wires can nucleate at the same temperature and pressure while much lower energy and matter budget is needed to grow same-size wires. This explains the experimental observations and may lead to energy- and matter-efficient synthesis of a broader range of one-dimensional quantum structures.

Self-organized quantum dot arrays : kinetic mapping of adatom capture

Deterministic synthesis of self-organized quantum dot arrays for renewable energy, biomedical, and optoelectronic applications requires control over adatom capture zones, which are presently mapped using unphysical geometric tessellation. In contrast, the proposed kinetic mapping is based on simulated two-dimensional adatom fluxes in the array and includes the effects of nucleation, dissolution, coalescence, and process parameters such as surface temperature and deposition rate. This approach is generic and can be used to control the nanoarray development in various practical applications. © 2009 American Institute of Physics.

Control of core-shell structure and elemental composition of binary quantum dots

The possibility of initial stage control of the elemental composition and core/shell structure of binary SiC quantum dots by optimizing temporal variation of Si and C incoming fluxes and surface temperatures is shown via hybrid numerical simulations. Higher temperatures and influxes encourage the formation of a stoichiometric outer shell over a small carbon-enriched core, whereas lower temperatures result in a larger carbon-enriched core, Si-enriched undershell, and then a stoichiometric SiC outer shell. This approach is generic and is applicable to a broad range of semiconductor materials and nanofabrication techniques. © 2007 American Institute of Physics.

Single-step, rapid low-temperature synthesis of Si quantum dots embedded in an amorphous SiC matrix in high-density reactive plasmas

A simple, effective and innovative approach based on low-pressure, thermally nonequilibrium, high-density inductively coupled plasmas is proposed to rapidly synthesize Si quantum dots (QDs) embedded in an amorphous SiC (a-SiC) matrix at a low substrate temperature and without any commonly used hydrogen dilution. The experimental results clearly demonstrate that uniform crystalline Si QDs with a size of 3-4 nm embedded in the silicon-rich (carbon content up to 10.7at.%) a-SiC matrix can be formed from the reactive mixture of silane and methane gases, with high growth rates of ∼1.27-2.34 nm s-1 and at a low substrate temperature of 200 °C. The achievement of the high-rate growth of Si QDs embedded in the a-SiC without any commonly used hydrogen dilution is discussed based on the unique properties of the inductively coupled plasma-based process. This work is particularly important for the development of the all-Si tandem cell-based third generation photovoltaic solar cells.

Increased size selectivity of Si quantum dots on SiC at low substrate temperatures : an ion-assisted self-organization approach

A simple, effective, and innovative approach based on ion-assisted self-organization is proposed to synthesize size-selected Si quantum dots (QDs) on SiC substrates at low substrate temperatures. Using hybrid numerical simulations, the formation of Si QDs through a self-organization approach is investigated by taking into account two distinct cases of Si QD formation using the ionization energy approximation theory, which considers ionized in-fluxes containing Si3+ and Si1+ ions in the presence of a microscopic nonuniform electric field induced by a variable surface bias. The results show that the highest percentage of the surface coverage by 1 and 2 nm size-selected QDs was achieved using a bias of -20 V and ions in the lowest charge state, namely, Si1+ ions in a low substrate temperature range (227-327 °C). As low substrate temperatures (≤500 °C) are desirable from a technological point of view, because (i) low-temperature deposition techniques are compatible with current thin-film Si-based solar cell fabrication and (ii) high processing temperatures can frequently cause damage to other components in electronic devices and destroy the tandem structure of Si QD-based third-generation solar cells, our results are highly relevant to the development of the third-generation all-Si tandem photovoltaic solar cells.

An investigation of goethite-seeded Al(OH)3 precipitation using in situ X-ray diffraction and Rietveld-based quantitative phase analysis

An in situ X-ray diffraction investigation of goethite-seeded Al(OH)3 precipitation from synthetic Bayer liquor at 343 K has been performed. The presence of iron oxides and oxyhydroxides in the Bayer process has implications for alumina reversion, which causes significant process losses through unwanted gibbsite precipitation, and is also relevant for the nucleation and growth of scale on mild steel process equipment. The gibbsite, bayerite and nordstrandite polymorphs of Al(OH)3 precipitated from the liquor; gibbsite appeared to precipitate first, with subsequent formation of bayerite and nordstrandite. A Rietveld-based approach to quantitative phase analysis was implemented for the determination of absolute phase abundances as a function of time, from which kinetic information for the formation of the Al(OH)3 phases was determined.

Integrated plasma-aided nanofabrication facility: Operation, parameters, and assembly of quantum structures and functional nanomaterials

The development, operation, and applications of two configurations of an integrated plasma-aided nanofabrication facility (IPANF) comprising low-frequency inductively coupled plasma-assisted, low-pressure, multiple-target RF magnetron sputtering plasma source, are reported. The two configurations of the plasma source have different arrangements of the RF inductive coil: a conventional external flat spiral "pancake" coil and an in-house developed internal antenna comprising two orthogonal RF current sheets. The internal antenna configuration generates a "unidirectional" RF current that deeply penetrates into the plasma bulk and results in an excellent uniformity of the plasma over large areas and volumes. The IPANF has been employed for various applications, including low-temperature plasma-enhanced chemical vapor deposition of vertically aligned single-crystalline carbon nanotips, growth of ultra-high aspect ratio semiconductor nanowires, assembly of optoelectronically important Si, SiC, and Al1-xInxN quantum dots, and plasma-based synthesis of bioactive hydroxyapatite for orthopedic implants.

The path to stoichiometric composition of III–V binary quantum dots through plasma/ion-assisted self-assembly

Semiconductor III-V quantum dots (QDs) are particularly enticing components for the integration of optically promising III-V materials with the silicon technology prevalent in the microelectronics industry. However, defects due to deviations from a stoichiometric composition [group III: group V = 1] may lead to impaired device performance. This paper investigates the initial stages of formation of InSb and GaAs QDs on Si(1 0 0) through hybrid numerical simulations. Three situations are considered: a neutral gas environment (NG), and two ionized gas environments, namely a localized ion source (LIS) and a background plasma (BP) case. It is shown that when the growth is conducted in an ionized gas environment, a stoichiometric composition may be obtained earlier in the QD as compared to a NG. Moreover, the stoichiometrization time, tst, is shorter for the BP case compared to the LIS scenario. A discussion of the effect of ion/plasma-based tools as well as a range of process conditions on the final island size distribution is also included. Our results suggest a way to obtain a deterministic level of control over nanostructure properties (in particular, elemental composition and size) during the initial stages of growth which is a crucial step towards achieving highly tailored QDs suitable for implementation in advanced technological devices.

Effect of elemental composition and size on electron confinement in self-assembled SiC quantum dots : a combinatorial approach

A high level of control over quantum dot (QD) properties such as size and composition during fabrication is required to precisely tune the eventual electronic properties of the QD. Nanoscale synthesis efforts and theoretical studies of electronic properties are traditionally treated quite separately. In this paper, a combinatorial approach has been taken to relate the process synthesis parameters and the electron confinement properties of the QDs. First, hybrid numerical calculations with different influx parameters for Si1-x Cx QDs were carried out to simulate the changes in carbon content x and size. Second, the ionization energy theory was applied to understand the electronic properties of Si1-x Cx QDs. Third, stoichiometric (x=0.5) silicon carbide QDs were grown by means of inductively coupled plasma-assisted rf magnetron sputtering. Finally, the effect of QD size and elemental composition were then incorporated in the ionization energy theory to explain the evolution of the Si1-x Cx photoluminescence spectra. These results are important for the development of deterministic synthesis approaches of self-assembled nanoscale quantum confinement structures.

Deterministic control of plasma-assembled self-organized Ge/Si quantum dot arrays

Self-assembly of size-uniform and spatially ordered quantum dot (QD) arrays is one of the major challenges in the development of the new generation of semiconducting nanoelectronic and photonic devices. Assembly of Ge QD (in the ∼5-20 nm size range) arrays from randomly generated position and size-nonuniform nanodot patterns on plasma-exposed Si (100) surfaces is studied using hybrid multiscale numerical simulations. It is shown, by properly manipulating the incoming ion/neutral flux from the plasma and the surface temperature, the uniformity of the nanodot size within the array can be improved by 34%-53%, with the best improvement achieved at low surface temperatures and high external incoming fluxes, which are intrinsic to plasma-aided processes. Using a plasma-based process also leads to an improvement (∼22% at 700 K surface temperature and 0.1 MLs incoming flux from the plasma) of the spatial order of a randomly sampled nanodot ensemble, which self-organizes to position the dots equidistantly to their neighbors within the array. Remarkable improvements in QD ordering and size uniformity can be achieved at high growth rates (a few nms) and a surface temperature as low as 600 K, which broadens the range of suitable substrates to temperature-sensitive ultrathin nanofilms and polymers. The results of this study are generic, can also be applied to nonplasma-based techniques, and as such contributes to the development of deterministic strategies of nanoassembly of self-ordered arrays of size-uniform QDs, in the size range where nanodot ordering cannot be achieved by presently available pattern delineation techniques.

Surface fluxes of Si and C adatoms at initial growth stages of SiC quantum dots

Self-assembly of highly stoichiometric SiC quantum dots still remains a major challenge for the gas/plasma-based nanodot synthesis. By means of a multiscale hybrid numerical simulation of the initial stage (0.1-2.5 s into the process) of deposition of SiCSi (100) quantum dot nuclei, it is shown that equal Si and kst atom deposition fluxes result in strong nonstoichiometric nanodot composition due to very different surface fluxes of Si and C adatoms to the quantum dots. At this stage, the surface fluxes of Si and C adatoms to SiC nanodots can be effectively controlled by manipulating the SiC atom influx ratio and the Si (100) surface temperature. It is demonstrated that at a surface temperature of 800 K the surface fluxes can be equalized after only 0.05 s into the process; however, it takes more then 1 s at a surface temperature of 600 K. Based on the results of this study, effective strategies to maintain a stoichiometric ([Si] [C] =1:1) elemental ratio during the initial stages of deposition of SiCSi (100) quantum dot nuclei in a neutral/ionized gas-based process are proposed.

Tailoring the composition of self-assembled Si1−xCx quantum dots : simulation of plasma/ion-related controls

Precise control of composition and internal structure is essential for a variety of novel technological applications which require highly tailored binary quantum dots (QDs) with predictable optoelectronic and mechanical properties. The delicate balancing act between incoming flux and substrate temperature required for the growth of compositionally graded (Si1-xC x; x varies throughout the internal structure), core-multishell (discrete shells of Si and C or combinations thereof) and selected composition (x set) QDs on low-temperature plasma/ion-flux-exposed Si(100) surfaces is investigated via a hybrid numerical simulation. Incident Si and C ions lead to localized substrate heating and a reduction in surface diffusion activation energy. It is shown that by incorporating ions in the influx, a steady-state composition is reached more quickly (for selected composition QDs) and the composition gradient of a Si1-xCx QD may be fine tuned; additionally (with other deposition conditions remaining the same), larger QDs are obtained on average. It is suggested that ionizing a portion of the influx is another way to control the average size of the QDs, and ultimately, their internal structure. Advantages that can be gained by utilizing plasma/ion-related controls to facilitate the growth of highly tailored, compositionally controlled quantum dots are discussed as well.

Nanopowder management and plasma parameters in nanofabrication of low-dimensional quantum structures in reactive silane-based plasmas

Management of nanopowder and reactive plasma parameters in a low-pressure RF glow discharge in silane is studied. It is shown that the discharge control parameters and reactor volume can be adjusted to ensure lower abundance of nanopowders, which is one of the requirements of the plasma-assisted fabrication of low-dimensional quantum nanostructures. The results are relevant to micro- and nanomanufacturing technologies employing low-pressure glow discharge plasmas of silane-based gas mixtures.

Ge/Si quantum dot formation from non-uniform cluster fluxes

The controlled growth of ultra-small Ge/Si quantum dot (QD) nuclei (≈1 nm) suitable for the synthesis of uniform nanopatterns with high surface coverage, is simulated using atom-only and size non-uniform cluster fluxes. It is found that seed nuclei of more uniform sizes are formed when clusters of non-uniform size are deposited. This counter-intuitive result is explained via adatom-nanocluster interactions on Si(100) surfaces. Our results are supported by experimental data on the geometric characteristics of QD patterns synthesized by nanocluster deposition. This is followed by a description of the role of plasmas as non-uniform cluster sources and the impact on surface dynamics. The technique challenges conventional growth modes and is promising for deterministic synthesis of nanodot arrays.

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.