6 research outputs found
Formation of silicon nanodots via ion beam sputtering of ultrathin gold thin film coatings on Si
Ion beam sputtering of ultrathin film Au coatings used as a physical catalyst for self-organization of Si nanostructures has been achieved by tuning the incident particle energy. This approach holds promise as a scalable nanomanufacturing parallel processing alternative to candidate nanolithography techniques. Structures of 11- to 14-nm Si nanodots are formed with normal incidence low-energy Ar ions of 200 eV and fluences above 2 × 1017 cm-2. In situ surface characterization during ion irradiation elucidates early stage ion mixing migration mechanism for nanodot self-organization. In particular, the evolution from gold film islands to the formation of ion-induced metastable gold silicide followed by pure Si nanodots formed with no need for impurity seeding
Exploiting Network Boundaries for Spectral Clustering and Tensor Network Generative Modeling
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Exploiting Network Boundaries for Spectral Clustering and Tensor Network Generative Modeling
In many systems, both physical and mathematical, internal boundaries play an outsized role in dictating the large-scale behavior of the system. This theme is explored here in two parts: by identifying natural boundaries to diffusion on undirected graphs for the improvement of spectral graph methods for clustering and classification, and by imposing boundaries in two dimensional PEPS tensor networks to reduce their computational complexity for image generation. In the graph diffusion work, an algorithm is developed that identifies and removes vertices serving as bridges between well-connected clusters. With the use of this algorithm, the performance of unsupervised spectral clustering on graphs derived from synthetic point cloud data shows excellent cluster separation down to approximately two-thirds the point cloud gap size that standard spectral clustering alone tolerates. The same vertex-removal scheme applied to a diffusion-informed active learning classification algorithm shows an approximately order-of-magnitude best-case reduction in the classification error rate on benchmark hyperspectral imagery data in the low-label-query-limit versus the same active learning algorithm applied without vertex removal. The PEPS work proposes a scheme whereby such networks are cut into overlapping patch networks, whose individual contraction complexity is comparatively low. Feasibility testing is performed showing that in principle, this scheme could be used for image in-painting, and has intuitively appealing model features that are directly reflective of the data
Exploiting Network Boundaries for Spectral Clustering and Tensor Network Generative Modeling
The significance of in situ conditions in the characterization of GaSb nanopatterned surfaces via ion beam sputtering
A systematic study is conducted in order to elucidate the underlying mechanism(s) for nanopatterning with low-energy irradiation of GaSb (100) under normal incidence. Ion energies between 50 and 1000 eV of Ar+ and ion fluences of up to 10(18) cm(-2) were employed. Characterization of the shallow (e.g., 1 to 6 nm) amorphous phase region induced by irradiation and the subsurface crystalline phase region is accomplished with low-energy ion scattering spectroscopy and x-ray photoelectron spectroscopy, respectively. In situ studies are conducted due to the strong chemical affinity for oxygen of GaSb. The studies conclude that at energies below 200 eV, the native oxide layer hampers nanopatterning until it becomes removed at a fluence of approximately 5 x 10(16) cm(-2). At this energy and threshold fluence, the surface is enriched with Ga atoms during irradiation. At energies above 200 eV, the native oxide layer is efficiently removed in the early irradiation stages, and thus the detrimental effects from the oxide on nanopatterning are negligible. In situ surface concentration quantification indicates that the surface enrichment with Sb atoms in the amorphous phase layer increases with the incident ion energy. Post-air exposure characterization reveals that the measured enrichment of the surface with gallium is due to oxygen reduction by Ga atoms segregated from both the amorphous and the crystalline phase regions as a result of air exposure. (C) 2011 American Institute of Physics. [doi:10.1063/1.3642997