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In addition to lithography process and equipment induced variations, processes like etching, annealing, film deposition and planarization exhibit variations, each having their own intrinsic characteristics and leaving an effect, a ‘fingerprint’, on the wafers. With ever tighter requirements for CD and overlay, controlling these process induced variations is both increasingly important and increasingly challenging in advanced integrated circuit (IC) manufacturing. For example, the on-product overlay (OPO) requirement for future nodes is approaching

An incremental/online state dynamic learning method is proposed for identification of the nonlinear Gaussian state space models. The method embeds the stochastic variational sparse Gaussian process as the probabilistic state dynamic model inside a particle filter framework. Model updating is done at measurement sample rate using stochastic gradient descent based optimization implemented in the state estimation filtering loop. The performance of the proposed method is compared with state-of-the-art Gaussian process based batch learning methods. Finally, it is shown that the state estimation performance significantly improves due to the online learning of state dynamics., submitted to IEEE Signal Processing Letters

Interaction analysis of ships mooring and maneuvering in harbors is pursued in this paper by using Bayesian probabilistic models. A number of ship-to-ship interactions are deduced from the navigation rules in port areas, and then used to train different Event-based Dynamic Bayesian Networks (E-DBNs). When data of two interacting ships are injected into the network, inference is performed in order to verify if the interaction between the vessels is known or not, and in the latter case actions to preserve the port safety can be taken. Results are drawn in the final part of the paper by including into the networks data provided by a simulator of realistic trajectories relative to an existing port.

Automatic recognition of abnormal situations in harbor environments is approached in this paper with a system based on Dynamic Bayesian Networks. The area under surveillance is partitioned in zones of different sizes and shapes by means of an Instantaneous Topological Map, on which events are detected and inference is carried out. The model is trained with synthetic normal trajectories of ships and vessels mooring in the port, and each time a new trajectory is presented to the system, comparisons with the normal behaviors stored in the network are performed. If no match is found, an abnormal situation is declared and countermeasures can be taken. The algorithm has been tested in a real port with simulated data in order to evaluate the false alarm rate and the abnormal detection capabilities of the proposed approach.