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We report on a simple and cost-effective method to fabricate high density silicon nanowires (SiNWs) through catalytic chemical wet etching. Metallic chromium/gold (Cr/Au) nanodots were first deposited onto the silicon wafer using an anodic aluminum oxide (AAO) template. The AAO template was then removed before a thin blanket layer of gold catalyst was evaporated onto the sample surface. The gold-assisted chemical wet etching was carried out in a solution consisting of deionized water, hydrogen peroxide, and hydrofluoric acid to produce well-aligned silicon nanowires of uniform diameters. We demonstrate that the diameter of the silicon nanowires can be precisely controlled to a precision of 10 nm in the range of 40 to 80 nm through fine-tuning of the pore diameter of the AAO template. The reported fabrication procedure therefore gives a highly repeatable method to form well-aligned, uniform, and crystalline SiNWs of high density with controllable diameters below 100 nm. The use of Cr/Au as a hard mask blocking material will also be of great interest for the fabrication of other Si nanostructures using the catalytic etching process.

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This paper describes a hybrid methodology that integrates genetic algorithms (GAs) and decision tree learning in order to evolve useful subsets of discriminatory features for recognizing complex visual concepts. A GA is used to search the space of all possible subsets of a large set of candidate discrimination features. Candidate feature subsets are evaluated by using C4.5, a decision tree learning algorithm, to produce a decision tree based on the given features using a limited amount of training data. The classification performance of the resulting decision tree on unseen testing data is used as the fitness of the underlying feature subset. Experimental results are presented to show how increasing the amount of learning significantly improves feature set evolution for difficult visual recognition problems involving satellite and facial image data. In addition, we also report on the extent to which other more subtle aspects of the Baldwin effect are exhibited by the system.

Growth of semiconductor nanowires has attracted immense attention in the field of nanotechnology as nanowires are viewed as the potential basic building blocks of future electronics. The recent renewed interest in germanium as a material for nanostructures can be attributed to its higher carrier mobility and larger Bohr radius as compared to silicon. Self-assembly synthesis of germanium nanowires (GeNWs) is often obtained through a vapor-liquid-solid mechanism, which is essentially a catalytic tip-growth process. Here we demonstrate that by introducing an additional precursor, germanium tetraiodide (GeI(4)), in a conventional furnace system that produces GeNWs on silicon, tubular structures of germanium-silicon (GeSi) oxide can be obtained instead. Incorporation of GeI(4) results in passivation of the metal catalyst, preventing the occurrence of supersaturation, a prerequisite for the catalytic tip growth. We infer that passivation of the metal catalyst impedes Ge incorporation into the catalyst, leaving the catalyst rim as the only active sites for nucleation of both Si and Ge and thus resulting in the growth of GeSi oxide nanotubes via a root-growth process.