To develop porous Si as substrate for better quality GaN layer
In this work, we prepare porous Si substrates via anodic etching process. The etching parameters such as time and current would be optimized for the best porous substrate, which is defined by having high porous density. The estimated average of porous density of the substrate is determined through the observation via scanning electron microscopy (SEM). After that, a thin GaN film is deposited onto the best porous Si substrate, using RF-sputtering machine. The grown layer is then will be characterized by photoluminescence (PL), X-ray diffraction (XRD) and Hall-effect measurements. As a comparison, we also grow and characterize a thin GaN on non-porous Si substrate, with the adoption of AlN buffer layer.
High quality GaN layers on Si(111) substrates: AlN buffer layer optimisation and insertion of a SiN intermediate layer
We present a study on the material properties of GaN films grown on (111) silicon substrates by low-pressure metalorganic chemical vapour deposition using AlN buffer layers. This buffer layer is optimised with respect to growth temperature and time for the optical and structural properties of the GaN epilayers. The insertion of a Si x N y intermediate layer significantly increases the optical and structural properties. It results in a reduction of the D 0 X FWHM to 10 meV and in a 2.5-fold increase of its luminescence intensity. The FWHM of symmetric and asymmetric ω-scans are reduced from 832 to 669 arcsec and from 702 to 547 arcsec, respectively.
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.