A high-performance 600 V smart power technology has been developed in which novel lateral double-diffused MOS transistors (LDMOS) are merged with a BiC-MOS process flow for the construction of power integrated circuits on bonded silicon-on-insulator (BSOI) substrates. All active and passive device structures have been optimized for fabrication on BSOI layers which are less than 1.5 pm-thick, with buried oxide layers in the range of 2.0 to 3.0 pm-thick. Complete dielectric isolation processing is straightforward due to the use of a thin SO1 active device layer. A dual field plate design of the high-voltage devices results in at least a factor-of-two reduction in specific onresistance over conventional LDMOS structures for a given breakdown voltage.

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The objective of this work is the development of an algorithm that, after training, will be able to discriminate between disease classes in molecular data. The system proposed uses a genetic algorithm (GA) to achieve this discrimination. We apply our method to three publicly available data sets. Two of the data sets are based on microarray data that allow the simultaneous measurement of the expression levels of genes under different disease states. The third data set is based on serum proteomic pattern diagnostics of ovarian cancer using highresolution mass spectrometry to extract a set of biomarker classifiers. We show how our methodology finds an abundance of different feature models, automatically selecting a subset of discriminatory features, whose classification accuracy is comparable to other approaches considered. This raises questions about how to choose among the many competing models, while simultaneously estimating the prediction accuracy of the chosen models.