Rediscovered in the last decade, zinc oxide (ZnO) shows a great potential for many optoelectronics and to some extent microelectronics applications. However, a clear majority of effort expended in this fast developing field has been limited to heteroepitaxial structures grown on foreign substrates with lattice-parameter and thermal-expansion mismatch with ZnO which is detrimental. Recognizing the importance, the effort has shifted to include developing technologies capable of producing freestanding ZnO wafers in large-scale for ZnO based device applications, which is the subject matter of this manuscript. Three competing approaches - hydrothermal method, melt growth (modifications of the well known Bridgman technique), and seeded vapor transport growth - have now reached or are approaching commercial viability. In this article, we discuss the progress, outstanding problems, and prospects of these growth methods employed for commercial manufacturing of ZnO wafers.

In this paper, we show the blood-cell image classification system to be able to analyze and distinguish blood cells in the peripheral blood image. To distinguish their abnormalities, we segment red and white-blood cell in an image acquired from microscope with CCD camera and then, apply the various feature extraction algorithms to classify them. In addition to, we use neural network model to reduce multi-variate feature number based on PCA(Principal Component Analysis) to make classifier more efficient. Finally we show that our system has a good experimental result and can be applied to build an aiding system for pathologist.