Highly ordered nanostructures with tunable size, shape and properties: A new way to surface nano-patterning using ultra-thin alumina masks

Large-scale arrays of nanostructures on Substrates. such as semiconductor or metal nano-particle arrays, have attracted considerable interest due to their unique physical properties and many potential applications in areas Such as electronics, optoelectronics, sensing, high-density storage, and ultra-thin display devices. In the last two decades, the search for a highly efficient and low-cost nano-patterning method in fabricating ordered surface nanostructures with tunable dimensions and properties, has involved interdisciplinary and cross-disciplinary research and development with emerging technologies such as lithographic methods, self-assembly processes, and scanning probe techniques. Here, we review a new surface nano-patterning approach in fabricating ordered nanostructures, in which ultra-thin anodic alumina membranes are used as fabrication masks. Using the method, large-scale arrays of highly ordered nanostructures in the range of square centimeters can be fabricated on any substrate in a massive parallel way. The resulting nanostructures are characterized by highly defined and controllable size, shape, composition, and spacing of the nanostructures. Tuning of the properties of the arrayed nanostructures can be obtained by controlled adjustment of the structural parameters of the arrayed nanostructures. Compared to conventional lithographic methods, the present nano-patterning approach offers attractive advantages, such as large pattern area, high throughput, low equipment costs, and high flexibility and control options for ordered nanostructures with tunable properties. This new non-lithographic nano-patterning approach will be shown to be a general method in fabricating a wide range of ordered surface nanostructures with tunable and unique physical and chemical properties that could be used in the fabrication of nano-devices with high performance and controllability. (c) 2006 Elsevier Ltd. All rights reserved.