bfPerovskites exhibit a wide range of remarkable material properties that have the potential to advance various scientific fields. These properties originate in their unique structure and composition. To leverage these properties in the ultrathin film regime, atomic-level control of thickness, composition, and crystal structure will be essential for creating next-generation perovskite devices. Atomic layer deposition (ALD) has the potential to enable these design prospects. However, its future use in the field will be dependent on the quality of the link between ALD process parameters and the perovskite phase.In this overview, we present work on barium and strontium titanate (BTO and STO) ultrathin films for high-k applications. We present ALD process strategies developed and optimized to achieve both desired composition and phase, yielding high dielectric constants and low leakage currents at the same time. We discuss thermal annealing, plasma treatment, and the use of seed layers and specialized precursors to improve the properties of BTO and STO by different enhancement mechanisms. In the ultrathin film regime, the understanding of macroscopic material properties will be dependent on the knowledge of the atomic scale arrangement. In conjunction with advances in manufacturing, we therefore also discuss novel strategies and techniques for characterization that will likely be significant in establishing a valid and reliable ALD process parameter-thin film dielectric property relationship.

In this work we wish to recover an unknown image from a blurry, or noisy-blurry version. We solve this inverse problem by energy minimization and regularization. We seek a solution of the form u + v, where u is a function of bounded variation (cartoon component), while v is an oscillatory component (texture), modeled by a Sobolev function with negative degree of differentiability. We give several results of existence and characterization of minimizers of the proposed optimization problem. Experimental results show that this cartoon + texture model better recovers textured details in natural images, by comparison with the more standard models where the unknown is restricted only to the space of functions of bounded variation.

Abstract.A new class of anisotropic diffusion models is proposed for image processing which can be viewed either as a novel kind of regularization of the classical Perona-Malik model or, as advocated by the authors, as a new independent model. The models are diffusive in nature and are characterized by the presence of both forward and backward regimes. In contrast to the Perona-Malik model, in the proposed model the backward regime is confined to a bounded region, and gradients are only allowed to grow up to a large but tunable size, thus effectively preventing indiscriminate singularity formation, i.e., staircasing. Extensive numerical experiments demonstrate that the method is a viable denoising/deblurring tool. The method is significantly faster than competing state-of-the-art methods and appears to be particularly effective for simultaneous denoising and deblurring. An application to satellite image enhancement is also presented.