Fabrication of novel double-hetero-epitaxial SOT structure Si/gamma-Al2O3/Si

In this paper, we report the fabrication of Si-based double-hetero-epitaxial silicon on insulator (SOI) structure Si/gamma-Al2O3/Si. Firstly, single crystalline gamma-Al2O3(100) insulator films were grown epitaxially on Si(100) using the sources of TMA (Al(CH3)(3)) and O-2 by very low-pressure chemical vapor deposition. Afterwards, Si(100) epitaxial films were grown on gamma-Al2O3 (100)/Si(100) epi-substrates using a chemical vapor deposition method similar to the silicon on sapphire epitaxial growth. The Si/gamma-Al2O3/Si SOL materials are characterized in detail by reflect high-energy electron diffraction, X-ray diffraction and Auger energy spectrum (AES) techniques. The insulator layer of gamma-Al2O3 has an excellent dielectric property. The leakage current is less than 1 x 10(-10) A/cm(2) when the electric field is below 1.3 MV/ cm. The Si film grown on gamma-Al2O3/Si epi-substrates was single crystalline. Meanwhile, the AES depth profile of the SOL structure shows that the composition of gamma-Al2O3 film is uniform, and the carbon contamination is not observed. Additionally, the gamma-Al2O3/Si epi-substrates are suitable candidates as a platform for a variety of active layers such as GaN, SiC and GeSi. It shows a bright future for microelectronic and optical electronics applications. (C) 2002 Elsevier Science B.V. All rights reserved.

Mathematical symbols and computing methods in high dimensional biomimetic informatics and their applications

High dimensional biomimetic informatics (HDBI) is a novel theory of informatics developed in recent years. Its primary object of research is points in high dimensional Euclidean space, and its exploratory and resolving procedures are based on simple geometric computations. However, the mathematical descriptions and computing of geometric objects are inconvenient because of the characters of geometry. With the increase of the dimension and the multiformity of geometric objects, these descriptions are more complicated and prolix especially in high dimensional space. In this paper, we give some definitions and mathematical symbols, and discuss some symbolic computing methods in high dimensional space systematically from the viewpoint of HDBI. With these methods, some multi-variables problems in high dimensional space can be solved easily. Three detailed algorithms are presented as examples to show the efficiency of our symbolic computing methods: the algorithm for judging the center of a circle given three points on this circle, the algorithm for judging whether two points are on the same side of a hyperplane, and the algorithm for judging whether a point is in a simplex constructed by points in high dimensional space. Two experiments in blurred image restoration and uneven lighting image correction are presented for all these algorithms to show their good behaviors.

Biomimetic pattern recognition theory and its applications

Biomimetic pattern recogntion (BPR), which is based on "cognition" instead of "classification", is much closer to the function of human being. The basis of BPR is the Principle of homology-continuity (PHC), which means the difference between two samples of the same class must be gradually changed. The aim of BPR is to find an optimal covering in the feature space, which emphasizes the "similarity" among homologous group members, rather than "division" in traditional pattern recognition. Some applications of BPR are surveyed, in which the results of BPR are much better than the results of Support Vector Machine. A novel neuron model, Hyper sausage neuron (HSN), is shown as a kind of covering units in BPR. The mathematical description of HSN is given and the 2-dimensional discriminant boundary of HSN is shown. In two special cases, in which samples are distributed in a line segment and a circle, both the HSN networks and RBF networks are used for covering. The results show that HSN networks act better than RBF networks in generalization, especially for small sample set, which are consonant with the results of the applications of BPR. And a brief explanation of the HSN networks' advantages in covering general distributed samples is also given.

High dimensional imagery geometry and it's applications

Because of information digitalization and the correspondence of digits and the coordinates, Information Science and high-dimensional space have consanguineous relations. With the transforming from the information issues to the point analysis in high-dimensional space, we proposed a novel computational theory, named High dimensional imagery geometry (HDIG). Some computational algorithms of HDIG have been realized using software, and how to combine with groups of simple operators in some 2D planes to implement the geometrical computations in high-dimensional space is demonstrated in this paper. As the applications, two kinds of experiments of HDIG, which are blurred image restoration and pattern recognition ones, are given, and the results are satisfying.