Formation of 3D Ge quantum dots array for advanced photovolatics in layer-cake technique
Placement of quantum dots (QDs) and insight into QD's basic internal structure and optical properties lay nature cornerstones for advanced photonic devices. We report a manageable growth method for placing dense three-dimensional Ge QD arrays in a uniform or a grading size distribution, using thermal oxidation of poly-SiGe in layer-cake techniques. The QD size and spatial density in each stack could be well modulated by Ge content in poly-Si1−xGex, oxidation and underlay buffer layer conditions. Size-dependent internal structure, strain, and photoluminesce properties of Ge QDs are systematically investigated. Optimization of processing conditions was carried out for producing dense Ge QD arrays for maximizing photovoltaic efficiency.
Miniband formulation in Ge/Si quantum dot array
In this work, we estimate the coupling effect of miniband structure and density of states (DoS) resulting from different qunatum-dot physical parameters in a well-aligned Ge/Si quantum dot (QD) array fabricated by neutral beam etching technology. The density of QDs dominates the coupling effect and miniband’s bandwidth, the radius of QDs affects the magnitude of energy levels and miniband bandwidth, and the thickness of QDs has a great impact on the magnitude of energy levels. Among the different shapes of Ge/Si QDs, discoid QDs exhibit the most band crossing and broadest bandwith under the same physical parameters.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.