Grazing Incidence Small Angle X-Ray Scattering (GISAXS) on Small Targets Using Large Beams

GISAXS is often used as a versatile tool for the contactless and destruction-free investigation of nanostructured surfaces. However, due to the shallow incidence angles, the footprint of the X-ray beam is significantly elongated, limiting GISAXS to samples with typical target lengths of several millimetres. For many potential applications, the production of large target areas is impractical, and the targets are surrounded by structured areas. Because the beam footprint is larger than the targets, the surrounding structures contribute parasitic scattering, burying the target signal. In this paper, GISAXS measurements of isolated as well as surrounded grating targets in Si substrates with line lengths from $50\,{\rm\mu m}$ down to $4\,{\rm\mu m}$ are presented. For the isolated grating targets, the changes in the scattering patterns due to the reduced target length are explained. For the surrounded grating targets, the scattering signal of a $15\,{\rm\mu m}\,\times\,15\,{\rm\mu m}$ target grating structure is separated from the scattering signal of $100\,{\rm\mu m}\,\times\,100\,{\rm\mu m}$ nanostructured surroundings by producing the target with a different orientation with respect to the predominant direction of the surrounding structures. The described technique allows to apply GISAXS, e.g. for characterization of metrology fields in the semiconductor industry, where up to now it has been considered impossible to use this method due to the large beam footprint

Reduction of slaughterhouse stress in beef cattle by facilitating animal tameness

The relationship between animals and humans is important for animal husbandry and welfare. Loosehousing and grazing systems with low management input often result in frail relationships between humans and animals. This study investigated whether a positive handling, applied during the first days of the animals’ life, had a calming and stress reducing effect on suckler beef calves at slaughter

A Multilevel Introspective Dynamic Optimization System For Holistic Power-Aware Computing

Power consumption is rapidly becoming the dominant limiting factor for further improvements in computer design. Curiously, this applies both at the "high end" of workstations and servers and the "low end" of handheld devices and embedded computers. At the high-end, the challenge lies in dealing with exponentially growing power densities. At the low-end, there is a demand to make mobile devices more powerful and longer lasting, but battery technology is not improving at the same rate that power consumption is rising. Traditional power-management research is fragmented; techniques are being developed at specific levels, without fully exploring their synergy with other levels. Most software techniques target either operating systems or compilers but do not explore the interaction between the two layers. These techniques also have not fully explored the potential of virtual machines for power management. In contrast, we are developing a system that integrates information from multiple levels of software and hardware, connecting these levels through a communication channel. At the heart of this system are a virtual machine that compiles and dynamically profiles code, and an optimizer that reoptimizes all code, including that of applications and the virtual machine itself. We believe this introspective, holistic approach enables more informed power-management decisions

Opening the black box: Unpacking board involvement in innovation

Corporate governance research suggests that boards of directors play key roles in governing company strategy. Although qualitative research has examined board-management relationships to describe board involvement in strategy, we lack detailed insights into how directors engage with organizational members for governing a complex and long-term issue such as product innovation. Our multiple-case study of four listed pharmaceutical firms reveals a sequential process of board involvement: Directors with deep expertise govern scientific innovation, followed by the full board's involvement in its strategic aspects. The nature of director involvement varies across board levels in terms of the direction (proactive or reactive), timing (regular or spontaneous), and the extent of formality of exchanges between directors and organizational members. Our study contributes to corporate governance research by introducing the concept of board behavioral diversity and by theorizing about the multilevel, structural, and temporal dimensions of board behavior and its relational characteristics

Correlated Diffuse X-ray Scattering from Periodically Nano-Structured Surfaces

Laterally periodic nanostructures were investigated with grazing incidence small angle X-ray scattering. To support an improved reconstruction of nanostructured surface geometries, we investigated the origin of the contributions to the diffuse scattering pattern which is correlated to the surface roughness. Resonant diffuse scattering leads to a palm-like structure of intensity sheets. Dynamic scattering generates the so-called Yoneda band caused by a resonant scatter enhancement at the critical angle of total reflection and higher-order Yoneda bands originating from a subsequent diffraction of the Yoneda enhanced scattering at the grating. Our explanations are supported by modelling using a solver for the time-harmonic Maxwell's equations based on the finite-element method

Reconstructing Detailed Line Profiles of Lamellar Gratings from GISAXS Patterns with a Maxwell Solver

Laterally periodic nanostructures were investigated with grazing incidence small angle X-ray scattering (GISAXS) by using the diffraction patterns to reconstruct the surface shape. To model visible light scattering, rigorous calculations of the near and far field by numerically solving Maxwell's equations with a finite-element method are well established. The application of this technique to X-rays is still challenging, due to the discrepancy between incident wavelength and finite-element size. This drawback vanishes for GISAXS due to the small angles of incidence, the conical scattering geometry and the periodicity of the surface structures, which allows a rigorous computation of the diffraction efficiencies with sufficient numerical precision. To develop dimensional metrology tools based on GISAXS, lamellar gratings with line widths down to 55 nm were produced by state-of-the-art e-beam lithography and then etched into silicon. The high surface sensitivity of GISAXS in conjunction with a Maxwell solver allows a detailed reconstruction of the grating line shape also for thick, non-homogeneous substrates. The reconstructed geometrical line shape models are statistically validated by applying a Markov chain Monte Carlo (MCMC) sampling technique which reveals that GISAXS is able to reconstruct critical parameters like the widths of the lines with sub-nm uncertainty

Using Evolutionary Strategies for the Real-Time Learning of Controllers for Autonomous Agents in Xpilot-AI

Real-time learning is the process of an artificial intelligence agent learning behavior(s) at the same pace as it operates in the real world. Video games tend to be an excellent locale for testing real-time learning agents, as the action happens at real speeds with a good visual feedback mechanism, coupled with the possibility of comparing human performance to that of the agent\u27s. In addition, players want to be competing against a consistently challenging opponent. This paper is a discussion of a controller for an agent in the space combat game Xpilot and the evolution of said controller using two different methods. The controller is a multilayer neural network, which controls all facets of the agent\u27s behavior that are not created in the initial set-up. The neural network is evolved using 1-to-1 evolutionary strategies in one method and genetic algorithms in the other method. Using three independent trials per methodology, it was shown that evolutionary strategies learned faster, while genetic algorithms learned more consistently, leading to the idea that genetic algorithms may be superior when there is ample time before use, but evolutionary strategies are better when pressed for learning time as in real-time learning