Dual material gate SOI MOSFET with asymmetrical halo can suppress short channel effect and increase carriers transport efficiency. The analytical model of its subthreshold drain current is derived based on the explicit solution of two-dimensional Poisson's equation in the depletion region. The model takes into consideration the channel length modulation effect and the contribution of the back channel current component. Its validation is verified by comparision with two dimensional device simulator MEDICI.

The quantum effect of carrier distribution in nanoscale SOI MOSFETs is evident and must be taken into consideration in device modeling and simulation. In this paper, a backpropagation neural network was applied to predict the quantum density of carriers from the classical density, and the influence of the network structure on training speed and accuracy was studied. It was concluded that a carefully trained neural network with two hidden layers using the Levenberg-Marquardt learning algorithm could predict the carrier quantum density of SOI MOSFETs in very good agreement with Schrdinger Poisson equations.

Objective To solve the three principal problems for reusing the software components, i.e., existence, discovery and availability principles, web services provided a new solution for reusing and assembling web software or components under distributed environment with a series of XML-based protocols. Methods A 4+1 meta-model between atomic services, such as Sequence, Parallelism, Alternation, Substitute and Iteration, and a kind of service composition description language in composition process are investigated. Furthermore, an architecture-based service composition model with Ontology Engine is proposed, which is based on the software architecture life-cycle model and provides a mechanism for services composition with dynamic management and deployment. Results The results of the prototype system development show that the service-composed application not only reused the web service components, but also reused the data resources located in distributed databases. Conclusion The method of reused-based web services composition observably decreases the period and cost of development and greatly improves the programming efficiency.

The quantum effect of carrier distribution in nanoscale SOI MOSFETs is evident and must be taken into consideration in device modeling and simulation. In this paper, a backpropagation neural network was applied to predict the quantum density of carriers from the classical density, and the influence of the network structure on training speed and accuracy was studied. It was concluded that a carefully trained neural network with two hidden layers using the Levenberg-Marquardt learning algorithm could predict the carrier quantum density of SOI MOSFETs in very good agreement with Schrödinger Poisson equations.