This paper presents a simulation study on the printing behavior of three different EUV resist systems. Stochastic models for negative metal-based resist and conventional chemically amplified resist (CAR) were calibrated and then validated. As for negative-tone development (NTD) CAR, we commenced from a positive-tone development (PTD) CAR calibrated (material) and NTD development models, since state-of-the-art measurements are not available. A conceptual study between PTD CAR and NTD CAR shows that the stochastic inhibitor fluctuation differs for PTD CAR: the inhibitor level exhibits small fluctuation (Mack development). For NTD CAR, the inhibitor fluctuation depends on the NTD type, which is defined by categorizing the difference between the NTD and PTD development thresholds. Respective NTD types have different inhibitor concentration level. Moreover, contact hole printing between negative metal-based and NTD CAR was compared to clarify the stochastic process window (PW) for tone reversed mask. For latter comparison, the aerial image (AI) and secondary electron effect are comparable. Finally, the local CD uniformity (LCDU) for the same 20 nm size, 40 nm pitch contact hole was compared among the three different resists. Dose-dependent behavior of LCDU and stochastic PW for NTD were different for the PTD CAR and metal-based resist. For NTD CAR, small inhibitor level and large inhibitor fluctuation around the development threshold were observed, causing LCDU increase, which is specific to the inverse Mack development resist.

Maneuvering target tracking under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions has received considerable interest in the last decades. In this paper, a hierarchical interacting multiple model (HIMM) method is proposed for estimating target position under mixed LOS/NLOS conditions. The proposed HIMM is composed of two layers with Markov switching model. The purpose of the upper layer, which is composed of two interacting multiple model (IMM) filters in parallel, is to handle the switching between the LOS and the NLOS environments. To estimate the target kinetic variables (position, speed and acceleration), the unscented Kalman filter (UKF) with the current statistical (CS) model is used in the lower-layer. Simulation results demonstrate the effectiveness and superiority of the proposed method, which obtains better tracking accuracy than the traditional IMM.

A new interacting multiple model (IMM) algorithm using intelligent input estimation (IIE) is proposed for maneuvering target tracking. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown target acceleration by a fuzzy system using the relation between the residuals of the maneuvering filter and the non-maneuvering filter. The genetic algorithm (GA) is utilized to optimize a fuzzy system for a sub-model within a fixed range of target acceleration. Then, multiple models are represented as the acceleration levels estimated by these fuzzy systems, which are optimized for different ranges of target acceleration. In computer simulation for an incoming anti-ship missile, it is shown that the proposed method has better tracking performance compared with the adaptive interacting multiple model (AIMM) algorithm.

This paper proposes a maneuvering target tracking algorithm using multiple model filters. This filtering algorithm is discussed in terms of tracking performance, tracking success rate and tracking accuracies for short sampling interval as compared with other conventional methodology. Through several simulations, validity of this algorithm has been confirmed.