350-nm band edge-emitting laser diodes enabled by low-dislocation-density AlGaN templates
Realization of efficient laser diodes with ultra-violet (UV) emission from ~260-360 nm would enable many applications including fluorescence-based biological agent detection, sterilization, and portable water purification. While InGaN-based laser diodes are well developed down to ~370 nm, achieving shorter UV wavelengths requires higher Al-content AlGaN alloys with increasing challenges in achieving p-type doping, strain-management, and low threading-dislocation-density (TDD) AlGaN templates. Given these challenges, few groups have reported AlGaN-based edge-emitting laser diodes (LDs) with emission <; 355 nm.[1, 2] Most recently, random lasing via Anderson localization in AlGaN nanowire structures has demonstrated a novel approach to realizing deep-UV laser diodes.[3].
Ultralow-threshold electrically injected AlGaN nanowire ultraviolet lasers on Si operating at low temperature.
Ultraviolet laser radiation has been adopted in a wide range of applications as diverse as water purification, flexible displays, data storage, sterilization, diagnosis and bioagent detection. Success in developing semiconductor-based, compact ultraviolet laser sources, however, has been extremely limited. Here, we report that defect-free disordered AlGaN core-shell nanowire arrays, formed directly on a Si substrate, can be used to achieve highly stable, electrically pumped lasers across the entire ultraviolet AII (UV-AII) band (∼320-340 nm) at low temperatures. The laser threshold is in the range of tens of amps per centimetre squared, which is nearly three orders of magnitude lower than those of previously reported quantum-well lasers. This work also reports the first demonstration of electrically injected AlGaN-based ultraviolet lasers monolithically grown on a Si substrate, and offers a new avenue for achieving semiconductor lasers in the ultraviolet B (UV-B) (280-320 nm) and ultraviolet C (UV-C) (<280 nm) bands.
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.