Ultra-broadband depolarization based on directly-coupled quantum wire-to-well modulation and their aliasing effect for polarization-insensitive light-emitting diodes.

Nowadays, strained quantum structures have been widely used in various light-emitting devices with a variety of compounds for progressive applications. However, the lattice-mismatch-induced strains in the materials would cause a problem of polarization dependence for polarization-independent optical applications. To address this issue, in this paper we propose a novel ultra-broadband depolarization mechanism and approach based on a directly-coupled well-wire-hybrid nanostructure. It contains quantum wire-to-well modulation and their aliasing effects on strain, energy-band structure and optical gain to obtain independent and comparable bipolarization of optical signals. The material structure involves a special well and on-well quantum wires with gradually-changing band-gaps, which are formed by utilizing the indium (In)-segregation effect and the growth-orientation-dependent multi-atomic step effect. With this special hybrid nanostructure, the depolarization efficiency can be 95% higher than that of a single compressive-strained quantum well. A low polarization degree of 0.05 and a very small gain difference of |GTE - GTM| < 1.3 cm-1 in different polarizations are achieved over a very broad gain bandwidth (870-950 nm) for an InGaAs material system. Therefore, this is a new chance for the development of ultra-broadband and polarization-insensitive optical applications.

High quality of an absolute phase reconstruction for coherent digital holography with an enhanced anti-speckle deep neural unwrapping network.

It is always a challenge how to overcome speckle noise interference in the phase reconstruction for coherent digital holography (CDH) and its application, as this issue has not been solved well so far. In this paper, we are proposing an enhanced anti-speckle deep neural unwrapping network (E-ASDNUN) approach to achieve high quality of absolute phase reconstruction for CDH. The method designs a special network-based noise filter and embeds it into a deep neural unwrapping network to enhance anti-noise capacity in the image feature recognition and extraction process. The numerical simulation and experimental test on the phase unwrapping reconstruction and the image quality evaluation under the noise circumstances show that the E-ASDNUN approach is very effective against the speckle noise in realizing the high quality of absolute phase reconstruction. Meanwhile, it also demonstrates much better robustness than the typical U-net neural network and the traditional phase unwrapping algorithms in reconstructing high wrapping densities and high noise levels of phase images. The E-ASDNUN approach is also examined and confirmed by measuring the same phase object using a commercial white light interferometry as a reference. The result is perfectly consistent with that obtained by the E-ASDNUN approach.