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Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning systems in terms of their power efficiency, parallelism and computational speed. Among them, free-space diffractive deep neural networks (D 2 NNs) based on the light diffraction, feature millions of neurons in each layer interconnected with neurons in neighboring layers. However, due to the challenge of implementing reconfigurability, deploying different DNNs algorithms requires re-building and duplicating the physical diffractive systems, which significantly degrades the hardware efficiency in practical application scenarios. Thus, this work proposes a novel hardware-software co-design method that enables first-of-its-like real-time multi-task learning in D 2 2NNs that automatically recognizes which task is being deployed in real-time. Our experimental results demonstrate significant improvements in versatility, hardware efficiency, and also demonstrate and quantify the robustness of proposed multi-task D 2 NN architecture under wide noise ranges of all system components. In addition, we propose a domain-specific regularization algorithm for training the proposed multi-task architecture, which can be used to flexibly adjust the desired performance for each task.

In this work, a systematic photoluminescence (PL) study on three series of gallium oxide/aluminum gallium oxide films and bulk single crystals is performed including comparing doping, epitaxial substrates, and aluminum concentration. It is observed that blue/green emission intensity strongly correlates with extended structural defects rather than the point defects frequently assumed. Bulk crystals or Si-doped films homoepitaxially grown on (010) β-Ga 2 O 3 yield an intense dominant UV emission, while samples with extended structural defects, such as gallium oxide films grown on either (-201) β-Ga 2 O 3 or sapphire, as well as thick aluminum gallium oxide films grown on either (010) β-Ga 2 O 3 or sapphire, all show a very broad PL spectrum with intense dominant blue/green emission. PL differences between samples and the possible causes of these differences are analyzed. This work expands previous reports that have so far attributed blue and green emissions to point defects and shows that in the case of thin films, extended defects might have a prominent role in emission properties.

While terahertz spectroscopy can provide valuable information regarding the charge transport properties in semiconductors, its application for the characterization of low-conductive two-dimensional layers, i.e., σ s  <  < 1 mS, remains elusive. This is primarily due to the low sensitivity of direct transmission measurements to such small sheet conductivity levels. In this work, we discuss harnessing the extraordinary optical transmission through gratings consisting of metallic stripes to characterize such low-conductive two-dimensional layers. We analyze the geometric tradeoffs in these structures and provide physical insights, ultimately leading to general design guidelines for experiments enabling non-contact, non-destructive, highly sensitive characterization of such layers.

We demonstrate ultra-thin (1.5-3λ 0 ), fabrication-error tolerant efficient diffractive terahertz (THz) optical elements designed using a computer-aided optimization-based search algorithm. The basic operation of these components is modeled using scalar diffraction of electromagnetic waves through a pixelated multi-level 3D-printed polymer structure. Through the proposed design framework, we demonstrate the design of various ultrathin planar THz optical elements, namely ( i ) a high Numerical Aperture (N.A.), broadband aberration rectified spherical lens (0.1 THz–0.3 THz), ( ii ) a spectral splitter (0.3 THz–0.6 THz) and ( iii ) an on-axis broadband transmissive hologram (0.3 THz–0.5 THz). Such an all-dielectric computational design-based approach is advantageous against metallic or dielectric metasurfaces from the perspective that it incorporates all the inherent structural advantages associated with a scalar diffraction based approach, such as ( i ) ease of modeling, ( ii ) substrate-less facile manufacturing, ( iii ) planar geometry, ( iv ) high efficiency along with (v) broadband operation, ( vi ) area scalability and ( vii ) fabrication error-tolerance. With scalability and error tolerance being two major bottlenecks of previous design strategies. This work is therefore, a significant step towards the design of THz optical elements by bridging the gap between structural and computational design i.e. through a hybrid design-based approach enabling considerably less computational resources than the previous state of the art. Furthermore, the approach used herein can be expanded to a myriad of optical elements at any wavelength regime.

The helical phase front of orbital angular momentum (OAM) waves offer additional multiplexing degree‐of‐freedom to increase the capacity of communication systems in the terahertz domain, which in turn can significantly benefit forthcoming high‐speed wireless sixth‐generation communication networks. This work introduces a diffractive neural network approach for recognizing the topological charge of OAM waves and their superposition. Moreover, it is shown that the diffractive network can further enable mathematical operations through the topological charges (TCs) of the superposed OAM waves. The diffractive neural networks (DNN) are fabricated through an imprinting technique with low‐loss parowax material. To validate the feasibility of this general approach, experimental demonstrations are conducted, which show that the low‐loss parowax DNN effectively detects the TCs of the OAM waves and display them in a numerical format. This work introduces a low‐loss diffractive neural network, fabricated using an imprinting technique with parowax material, for recognizing and manipulating the topological charge of orbital angular momentum (OAM) waves. It is also demonstrated that the low‐loss diffractive network can perform mathematical operations based on the topological charges of superposed OAM waves.

This study presents an experimentally validated demonstration of an inverse-optimized binary phase-only gallium oxide diffractive optical element (DOE). This DOE transforms an incident Gaussian beam into a square flat-top beam at the working plane. The design methodology for this binary phase-only DOE beam shaper is founded on an efficient process that integrates the modified Gerchberg-Saxton algorithm and the adjoint method. Experimental characterization of the fabricated device on a single crystal gallium oxide substrate is conducted at a wavelength of 532 nm, confirming its ability to transform an incident Gaussian beam into a focused square flat-top beam. Such a device holds significant promise for various high-power laser applications, notably in laser welding and similar domains. Furthermore, because of the ultrawide bandgap of gallium oxide, DOEs operating at shorter wavelengths in the UV are also possible based on this technique.

Multilevel diffractive lenses (MDLs) have emerged as an alternative to both conventional diffractive optical elements (DOEs) and metalenses for applications ranging from imaging to holographic and immersive displays. Recent work has shown that by harnessing structural parametric optimization of DOEs, one can design MDLs to enable multiple functionalities like achromaticity, depth of focus, wide-angle imaging, etc. with great ease in fabrication. Therefore, it becomes critical to understand how fabrication errors still do affect the performance of MDLs and numerically evaluate the trade-off between efficiency and initial parameter selection, right at the onset of designing an MDL, i.e., even before putting it into fabrication. Here, we perform a statistical simulation-based study on MDLs (primarily operating in the THz regime) to analyse the impact of various fabrication imperfections (single and multiple) on the final structure as a function of the number of ring height levels. Furthermore, we also evaluate the performance of these same MDLs with the change in the refractive index of the constitutive material. We use focusing efficiency as the evaluation criterion in our numerical analysis; since it is the most fundamental property that can be used to compare and assess the performance of lenses (and MDLs) in general designed for any application with any specific functionality.

Terahertz technology promises myriad applications including imaging, spectroscopy and communications. However, one major bottleneck at present for advancing this field is the lack of efficient devices to manipulate the terahertz electromagnetic waves. Here we demonstrate that exceptionally efficient broadband modulation of terahertz waves at room temperature can be realized using graphene with extremely low intrinsic signal attenuation. We experimentally achieved more than 2.5 times superior modulation than prior broadband intensity modulators, which is also the first demonstrated graphene-based device enabled solely by intraband transitions. The unique advantages of graphene in comparison to conventional semiconductors are the ease of integration and the extraordinary transport properties of holes, which are as good as those of electrons owing to the symmetric conical band structure of graphene. Given recent progress in graphene-based terahertz emitters and detectors, graphene may offer some interesting solutions for terahertz technologies. The terahertz spectral region is desirable for applications such as imaging or spectroscopy, but progress is hampered by a lack of efficient terahertz devices. By exploiting intraband transitions in graphene, Sensale-Rodriguez et al . demonstrate a broadband intensity modulator working at terahertz frequencies.

Pixelated RF metasurfaces are poised to revolutionize electromagnetic component design by enabling compact, versatile, high-performance solutions. Building upon our prior work in random metasurface-based filters and inverse design methods, we propose pixelated notch filters by integrating shorted stubs within a top ground plane. Using a combination of established optimization techniques, including direct binary search optimization, genetic algorithms, and a randomization mutation algorithm, we synthesize filters enhanced by parallel short-ended feed schemes, which are shown to improve stopband response. Design iterations are automated via Python scripting, commercial full-wave simulations, and Visual Basic within the electromagnetic solver, overcoming initial seeding challenges and enabling innovative pattern-generation techniques. For implementation, laser ablation is employed to precisely remove copper on PCBs, streamlining fabrication on Rogers Kappa 438 substrates. Preliminary results demonstrate the ability of the approach to reach target insertion loss levels with compact geometries, advancing pixelated metasurface-based filter design with enhanced tunability and overall performance.