Static High Voltage Actuation of Piezoelectric AlN and AlScN Based Scanning Micromirrors.

Piezoelectric micromirrors with aluminum nitride (AlN) and aluminum scandium nitride (AlScN) are presented and compared regarding their static deflection. Two chip designs with 2 × 3 mm (Design 1) and 4 × 6 mm (Design 2) footprint with 600 nm AlN or 2000 nm AlScN as piezoelectric transducer material are investigated. The chip with Design 1 and AlScN has a resonance frequency of 1.

2D Scanning Micromirror with Large Scan Angle and Monolithically Integrated Angle Sensors Based on Piezoelectric Thin Film Aluminum Nitride.

A 2D scanning micromirror with piezoelectric thin film aluminum nitride (AlN), separately used as actuator and sensor material, is presented. For endoscopic applications, such as fluorescence microscopy, the devices have a mirror plate diameter of 0.7 mm with a 4 mm chip footprint.

Supramarginal gyrus involvement in visual word recognition.

Introduction: In the classic neurological model of language, the human inferior parietal lobule (IPL) plays an important role in visual word recognition. The region is both functionally and structurally heterogeneous, however, suggesting that subregions of IPL may differentially contribute to reading. The two main sub-divisions are the supramarginal (SMG) and angular gyri, which have been hypothesized to contribute preferentially to phonological and semantic aspects of word processing, respectively.

Functional connectivity in tactile object discrimination: a principal component analysis of an event related fMRI-Study.

Background: Tactile object discrimination is an essential human skill that relies on functional connectivity between the neural substrates of motor, somatosensory and supramodal areas. From a theoretical point of view, such distributed networks elude categorical analysis because subtraction methods are univariate. Thus, the aim of this study was to identify the neural networks involved in somatosensory object discrimination using a voxel-based principal component analysis (PCA) of event-related functional magnetic resonance images.