In this paper we extend the previously introduced notion of closed-loop state sensitivity by introducing the concept of input sensitivity and by showing how to exploit it in a trajectory optimization framework. This allows to generate an optimal reference trajectory for a robot that minimizes the state and input sensitivities against uncertainties in the model parameters, thus producing inherently robust motion plans. We parametrize the reference trajectories with Béziers curves and discuss how to consider linear and nonlinear constraints in the optimization process (e.g., input saturations). The whole machinery is validated via an extensive statistical campaign that clearly shows the interest of the proposed methodology.

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The present review provides an overview of the basic theory of sputtering with recent models, focussing in particular on sputtered atom energy distribution functions. Molecular models such as Monte-Carlo, kinetic Monte-Carlo, and classical Molecular Dynamics simulations are presented due to their ability to describe the various processes involved in sputter deposition at the atomic and molecular scale as required. The sputter plasma, the sputtering mechanisms, the transport of sputtered material and its deposition leading to thin film growth can be addressed using these molecular simulations. In all cases, the underlying methodologies and some selected mechanisms are highlighted.