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Fluorine (F) ions which deduced from tungsten-polycide (W-polycide) formation and affected on metal-oxide-semiconductor (MOS) devices with pure oxide, oxynitride (NO) and re-oxidized oxynitride (ONO) gate dielectric under high field stress were investigated in detail. Although a significant amount of interface states were detected by charge pumping technique in NO and ONO device with W-polycide gate in comparison with that devices with polysilicon gate, however, it is found that NO, and especially ONO gate dielectric, can effectively improve the hot-carrier degradation in W-polycide gated MOS field-effect transistor (FET). This mechanism is well interpreted with a model about F ions increasing the nitrogen concentration at the interface between gate dielectric and Si substrate according to the result of secondary ion mass spectrometry (SIMS) analysis.

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Abstract. The blind signal separation problem (BSS) which involved linear mixing model and stationary source signals is focused in this paper. In the past, the neural network (NN) model is the popular architecture for separation, but its performance depends on initiation of weight strongly. In order to improve this problem to enhance global convergent, the genetic algorithm (GA) has been introduced for optimizing the weights of NN system recently. This paper, a novel evolution algorithm, particle swarm optimization (PSO) is introduced to optimize NN weights by us. Further, in simulation experiments of BSS, it is demonstrated that the PSO-based NN system has better performance in terms of global searching, computational time, accuracy and efficiency than the GA-based NN system.