Design and characterization of advanced diffractive devices for imaging and spectroscopy

Due to the ever-increasing demands of highly integrated optical devices in imaging, spectroscopy, communications, and so on, there is a compelling need to design and characterize novel compact photonic components. The traditional approaches to realizing compact optical devices typically result in large footprints and sizable optical thicknesses. Moreover, they offer few degrees of freedom (DOF), hampering on-demand functionalities, on-chip integration, and scalability. This thesis will address the design and development of ultracompact diffractive devices for imaging and spectroscopy, utilizing advanced machine learning techniques and optimization algorithms. I first present the inverse design of ultracompact dual-focusing lenses and broad-band focusing spectrometers based on adaptive diffractive optical networks (a-DONs), which combine optical diffraction physics and deep learning capabilities for the inverse design of multi-layered diffractive devices. I designed two-layer diffractive devices that can selectively focus incident radiation over well-separated spectral bands at desired distances and also optimized a-DON-based focusing spectrometers with engineered angular dispersion for desired bandwidth and nanometer spectral resolution. Furthermore, I introduced a new approach based on a-DONs for the engineering of diffractive devices with arbitrary k-space, which produces improved imaging performances compared to contour-PSF approaches to lens-less computational imaging. Moreover, my method enables control of sparsity and isotropic k-space in pixelated screens of dielectric scatterers that are compatible with large-scale photolithographic fabrication techniques. Finally, by combining adjoint optimization with the rigorous generalized Mie theory, I developed and characterize functionalized compact devices, which I called "photonic patches," consisting of ~100 dielectric nanocylinders that achieve predefined functionalities such as beam steering, Fresnel zone focusing, local density of states (LDOS) enhancement, etc. My method enables the inverse design of ultracompact focusing spectrometers for on-chip planar integration. Leveraging multiple scattering of light in disordered random media, I additionally demonstrated a novel approach to on-chip spectroscopy driven by high-throughput multifractal (i.e., multiscale) media, resulting in sub-nanometer spectral resolution at the 50×50 µm²-scale footprint

Design of ultracompact broadband focusing spectrometers based on deep diffractive neural networks

We propose the inverse design of ultracompact, broadband focusing spectrometers based on adaptive deep diffractive neural networks (a-D$^2$NNs). Specifically, we introduce and characterize two-layer diffractive devices with engineered angular dispersion that focus and steer broadband incident radiation along predefined focal trajectories with desired bandwidth and $5$ nm spectral resolution. Moreover, we systematically study the focusing efficiency of two-layer devices with side length $L=100~\mu\mathrm{m}$ and focal length $f=300~\,\mu\mathrm{m}$ across the visible spectrum and we demonstrate accurate reconstruction of the emission spectrum from a commercial superluminescent diode. The proposed a-D$^2$NNs design method extends the capabilities of efficient multi-focal diffractive optical devices to include single-shot focusing spectrometers with customized focal trajectories for applications to ultracompact multispectral imaging and lensless microscopy

Supply Chain Collaboration and Supplier Development in Chinese E-commerce Industry: a case study of JD.com

Abstract Due to the fact that supply chain collaboration (SCC) can achieve synergies and superior performance, it has become the trend of supply chain development and the goal for many companies. Especially, in the last 30 years, e-commerce has grown in popularity, its supply chain also tends to collaboration. Hence, the purpose of this dissertation is to analyze JD.com, the biggest direct sales e-commerce company in China, as a case study to research its supply chain collaboration condition and supplier management. There are six chapters to introduce and illustrate the supply chain collaboration of JD.com. This case will focus on what is JD.com operation model and its collaboration model? How does JD.com collaborate with their main and minor suppliers? Through collaborating, what benefits did JD.com and collaborating suppliers gain? What can we learn from JD.com SSC and supplier development? This paper concludes that there exist some issues in JD.com’s current supply chain collaboration and that SCC is not widely applied. Actually, all those existing problems are resulted from the immature development of e-commerce in China. Thereby, together with some knowledge background in literature review, we are offering some suggestions with regards to the current condition of JD.com to help them improve. Key words: supply chain collaboration; e-commerce; suppliers collaboration; information sharing; JD.co

High-throughput speckle spectrometers based on multifractal scattering media

Funding: S. A. Schulz and B. Kumar acknowledge funding from the EPSRC project EP/V029975/1: "Disorder enhanced on-chip spectrometers". L.D.N. acknowledges support from the National Science Foundation (ECCS-2015700, ECCS-2110204)".We present compact integrated speckle spectrometers based on monofractal and multifractal scattering media in a silicon-on-insulator platform. Through both numerical and experimental studies we demonstrate enhanced optical throughput, and hence signal-to-noise ratio, for a number of random structures with tailored multifractal geometries without affecting the spectral decay of the speckle correlation functions. Moreover, we show that the developed multifractal media outperform traditional scattering spectrometers based on uniform random distributions of scattering centers. Our findings establish the potential of low-density random media with multifractal correlations for integrated on-chip applications beyond what is possible with uncorrelated random disorder.Peer reviewe

Inverse design of functional photonic patches by adjoint optimization coupled to the generalized Mie theory

We propose a rigorous approach for the inverse design of functional photonic structures by coupling the adjoint optimization method and the two-dimensional generalized Mie theory (2D-GMT) for the multiple scattering problem of finite-size arrays of dielectric nanocylinders optimized to display desired functions. We refer to these functional scattering structures as "photonic patches". We briefly introduce the formalism of 2D-GMT and the critical steps necessary to implement the adjoint optimization algorithm to photonic patches with designed radiation properties. In particular, we showcase several examples of periodic and aperiodic photonic patches with optimal nanocylinder radii and arrangements for radiation shaping, wavefront focusing in the Fresnel zone, and for the enhancement of the local density of states (LDOS) at multiple wavelengths over micron-size areas. Moreover, we systematically compare the performances of periodic and aperiodic patches with different sizes and find that optimized aperiodic Vogel spiral geometries feature significant advantages in achromatic focusing compared to their periodic counterparts. Our results show that adjoint optimization coupled to 2D-GMT is a robust methodology for the inverse design of compact photonic devices that operate in the multiple scattering regime with optimal desired functionalities. Without the need of spatial meshing, our approach provides efficient solutions at strongly reduced computational burden compared to standard numerical optimization techniques and suggests compact device geometries for on-chip photonics and metamaterials technologies

Enhanced wave localization in multifractal scattering media

In this paper we study the structural, scattering, and wave localization properties of multifractal arrays of electric point dipoles generated from multiplicative random fields with different degrees of multiscale correlations. Specifically, using the rigorous Green's matrix method, we investigate the scattering resonances and wave localization behavior of systems with $N=10^{4}$ dipoles and demonstrate an enhanced localization behavior in highly inhomogeneous multifractal structures compared to homogeneous fractals, or monofractals. We show distinctive spectral properties, such as the absence of level repulsion in the strong multiple scattering regime and power-law statistics of level spacings, which indicate a clear localization transition enhanced in non-homogeneous multifractals. Our findings unveil the importance of multifractal structural correlations in the multiple scattering regime of electric dipole arrays and provide an efficient model for the design of multiscale nanophotonic systems with enhanced light-matter coupling and localization phenomena beyond what is possible with traditional fractal systems

UV R-CNN: Stable and Efficient Dense Human Pose Estimation

Dense pose estimation is a dense 3D prediction task for instance-level human analysis, aiming to map human pixels from an RGB image to a 3D surface of the human body. Due to a large amount of surface point regression, the training process appears to be easy to collapse compared to other region-based human instance analyzing tasks. By analyzing the loss formulation of the existing dense pose estimation model, we introduce a novel point regression loss function, named Dense Points} loss to stable the training progress, and a new balanced loss weighting strategy to handle the multi-task losses. With the above novelties, we propose a brand new architecture, named UV R-CNN. Without auxiliary supervision and external knowledge from other tasks, UV R-CNN can handle many complicated issues in dense pose model training progress, achieving 65.0% $AP_{gps}$ and 66.1% $AP_{gpsm}$ on the DensePose-COCO validation subset with ResNet-50-FPN feature extractor, competitive among the state-of-the-art dense human pose estimation methods.Comment: 9pages, 4 figure