In this paper, we have systematically investigated parasitic effects due to the gate and source-drain engineering in multi-gate transistors. The potential impact of high-K dielectrics on multi-gate MOSFETs (MuGFETs), such as FinFET, is evaluated through 2D and 3D device simulations over a wide range of proposed dielectric values. It is observed that introduction of high-K dielectrics will significantly degrade the short channel effects (SCEs), however a combination of oxide and high-K stack can effectively control this degradation. The degradation is mainly due to the increase in the internal fringe capacitance coupled with the decrease in gate-channel capacitance. From the circuit perspective, an optimum K value has been identified through mixed mode simulations. Further, as a part of this work, the importance of optimization of the shape of the spacer region is highlighted through full 3D simulations.

Particle filters have been widely used for state estimation problems in nonlinear and non-Gaussian systems. Their performance depends on the given system and measurement models, which need to be designed by the user for each target system. This paper proposes a novel method to design these models for a particle filter. This is a numerical optimization method, where the particle filter design process is interpreted into the framework of reinforcement learning by assigning the randomnesses included in both models of the particle filter to the policy of reinforcement learning. In this method, estimation by the particle filter is repeatedly performed and the parameters that determine both models are gradually updated according to the estimation results. The advantage is that it can optimize various objective functions, such as the estimation accuracy of the particle filter, the variance of the particles, the likelihood of the parameters, and the regularization term of the parameters. We derive the conditions to guarantee that the optimization calculation converges with probability 1. Furthermore, in order to show that the proposed method can be applied to practical-scale problems, we design the particle filter for mobile robot localization, which is an essential technology for autonomous navigation. By numerical simulations, it is demonstrated that the proposed method further improves the localization accuracy compared to the conventional method.