Search

Holography is capable of rendering three-dimensional scenes with full-depth control, and delivering transformative experiences across numerous domains, including virtual and augmented reality, education, and communication. However, traditional holography presents 3D scenes with unnatural defocus and severe speckles due to the limited space bandwidth product of the spatial light modulator (SLM). Here, we introduce Motion Hologram, a novel holographic technique to accurately portray photorealistic and speckle-free 3D scenes, by leveraging a single hologram and learnable motion trajectory, which are jointly optimized within the deep reinforcement learning framework. Specifically, we experimentally demonstrated the proposed technique could achieve a 4~5 dB PSNR improvement of focal stacks in comparison with traditional holography and could successfully depict speckle-free, high-fidelity, and full-color 3D displays using only a commercial SLM for the first time. We believe the proposed method promises a new form of holographic displays that will offer immersive viewing experiences for audiences., Comment: Under review

The generation and control of optical frequency combs in integrated photonic systems enables complex, high-controllable, and large-scale devices. In parallel, harnessing topological physics in multipartite systems has allowed them with compelling features such as robustness against fabrication imperfections. Here we experimentally demonstrate on-chip topological transport for optical frequency combs at telecommunication wavelengths, both in classical and nonclassical domains. We access both the quantum frequency combs and dissipative Kerr soliton combs with a micro-resonator. The quantum frequency comb, that is, a coherent superposition of multiple frequency modes, is proven to be a frequency-entangled qudit state. We also show that dissipative Kerr soliton combs are highly coherent and mode-locked due to the collective coherence or self-organization of solitons. Moreover, the valley kink states allow both quantum frequency combs and dissipative Kerr soliton combs with robustness against sharp bends. Our topologically protected optical frequency combs could enable the inherent robustness in integrated complex photonic systems., Comment: 20 pages,12 figures

Miniaturized spectrometry system is playing an essential role for materials analysis in the development of in-situ or portable sensing platforms across research and industry. However, there unavoidably exists trade-offs between the resolution and operation bandwidth as the device scale down. Here, we report an extreme miniaturized computational photonic molecule (PM) spectrometer utilizing the diverse spectral characteristics and mode-hybridization effect of split eigenfrequencies and super-modes, which effectively eliminates the inherent periodicity and expands operation bandwidth with ultra-high spectral resolution. These results of dynamic control of the frequency, amplitude, and phase of photons in the photonic multi-atomic systems, pave the way to the development of benchtop sensing platforms for applications previously unfeasible due to resolution-bandwidth-footprint limitations, such as in gas sensing or nanoscale biomedical sensing.

Non-Abelian geometry phase has attracted significant attention for the robust holonomic unitary behavior exhibited, which arises from the degenerate subspace evolving along a trajectory in Hilbert space. It has been regarded as a promising approach for implementing topologically protected quantum computation and logic manipulation. However, due to the challenges associated with high-dimensional parameters manipulation, this matrix-valued geometry phase has not been realized on silicon integrated photonic platform, which is CMOS compatible and regarded as the most promising flatform for next-generation functional devices. Here, we demonstrate the first non-Abelian holonomic high-dimensional unitary matrices on multilayer silicon nitride integrated platform. By leveraging the advantage of integrated platform and geometry phase, ultracompact footprint, highest order (up to six) and broadband operation (larger than 100nm) non-Abelian holonomy unitary matrices are experimentally realized. Our work paves the way for versatile non-Abelian optical computing devices in integrated photonics.

Silicon photonic integration has gained great success in many application fields owing to the excellent optical device properties and complementary metal-oxide semiconductor (CMOS) compatibility. Realizing monolithic integration of III-V lasers and silicon photonic components on single silicon wafer is recognized as a long-standing obstacle for ultra-dense photonic integration, which can provide considerable economical, energy efficient and foundry-scalable on-chip light sources, that has not been reported yet. Here, we demonstrate embedded InAs/GaAs quantum dot (QD) lasers directly grown on trenched silicon-on-insulator (SOI) substrate, enabling monolithic integration with butt-coupled silicon waveguides. By utilizing the patterned grating structures inside pre-defined SOI trenches and unique epitaxial method via molecular beam epitaxy (MBE), high-performance embedded InAs QD lasers with out-coupled silicon waveguide are achieved on such template. By resolving the epitaxy and fabrication challenges in such monolithic integrated architecture, embedded III-V lasers on SOI with continuous-wave lasing up to 85 oC are obtained. The maximum output power of 6.8 mW can be measured from the end tip of the butt-coupled silicon waveguides, with estimated coupling efficiency of approximately -7.35 dB. The results presented here provide a scalable and low-cost epitaxial method for realization of on-chip light sources directly coupling to the silicon photonic components for future high-density photonic integration.

Inpainting shadowed regions cast by superficial blood vessels in retinal optical coherence tomography (OCT) images is critical for accurate and robust machine analysis and clinical diagnosis. Traditional sequence-based approaches such as propagating neighboring information to gradually fill in the missing regions are cost-effective. But they generate less satisfactory outcomes when dealing with larger missing regions and texture-rich structures. Emerging deep learning-based methods such as encoder-decoder networks have shown promising results in natural image inpainting tasks. However, they typically need a long computational time for network training in addition to the high demand on the size of datasets, which makes it difficult to be applied on often small medical datasets. To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract features from a small amount of training images for further inpainting and to regularize the image after the multi-scale image fusion, while convolutional neural network (CNN) is employed to enhance the image quality. During the image inpainting, we divide preprocessed input images into different branches based on the shadow width to harvest complementary information from different scales. Finally, a sparse representation-based regularizing module is designed to refine the generated contents after multi-scale feature aggregation. Experiments are conducted to compare our proposal versus both traditional and deep learning-based techniques on synthetic and real-world shadows. Results demonstrate that our proposed method achieves favorable image inpainting in terms of visual quality and quantitative metrics, especially when wide shadows are presented.

Current learning-based single image super-resolution (SISR) algorithms underperform on real data due to the deviation in the assumed degrada-tion process from that in the real-world scenario. Conventional degradation processes consider applying blur, noise, and downsampling (typicallybicubic downsampling) on high-resolution (HR) images to synthesize low-resolution (LR) counterparts. However, few works on degradation modelling have taken the physical aspects of the optical imaging system intoconsideration. In this paper, we analyze the imaging system optically andexploit the characteristics of the real-world LR-HR pairs in the spatial frequency domain. We formulate a real-world physics-inspired degradationmodel by considering bothopticsandsensordegradation; The physical degradation of an imaging system is modelled as a low-pass filter, whose cut-off frequency is dictated by the object distance, the focal length of thelens, and the pixel size of the image sensor. In particular, we propose to use a convolutional neural network (CNN) to learn the cutoff frequency of real-world degradation process. The learned network is then applied to synthesize LR images from unpaired HR images. The synthetic HR-LR image pairs are later used to train an SISR network. We evaluatethe effectiveness and generalization capability of the proposed degradation model on real-world images captured by different imaging systems. Experimental results showcase that the SISR network trained by using our synthetic data performs favorably against the network using the traditional degradation model. Moreover, our results are comparable to that obtained by the same network trained by using real-world LR-HR pairs, which are challenging to obtain in real scenes., Comment: 22 pages,12 figures

In order to overcome the accommodation and convergence (A-C) conflict that commonly causes visual fatigue in AR display, we propose a Maxwellian-viewing-super-multi-view (MV-SMV) near-eye display system based on a Pancharatnam-Berry optical element (PBOE). The PBOE, which is constituted with an array of high-efficiency polarization gratings, is implemented to direct different views to different directions simultaneously, constructing the 3D light field. Meanwhile, each view is like a Maxwellian view display that possesses a small viewpoint and a large depth of field (DOF). Hence, the MV-SMV display can display virtual images with correct accommodation depth cue within a large DOF. We implement a proof-of-concept MV-SMV display prototype with 3 x 1 and 3 x 2 viewpoints using a 1D PBOE and a 2D PBOE, respectively, and achieve a DOF of 4.37 diopters experimentally.

An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma. However, due to the presence of the optic disc, the anatomical structure of the peripapillary region of the retina is complicated and is challenging for segmentation. To address this issue, we developed a novel graph convolutional network (GCN)-assisted two-stage framework to simultaneously label the nine retinal layers and the optic disc. Specifically, a multi-scale global reasoning module is inserted between the encoder and decoder of a U-shape neural network to exploit anatomical prior knowledge and perform spatial reasoning. We conducted experiments on human peripapillary retinal OCT images. The Dice score of the proposed segmentation network is 0.820$\pm$0.001 and the pixel accuracy is 0.830$\pm$0.002, both of which outperform those from other state-of-the-art techniques.

We experimentally demonstrate a record net capacity per wavelength of 1.23~Tb/s over a single silicon-on-insulator (SOI) multimode waveguide for optical interconnects employing on-chip mode-division multiplexing and 11$\times$11 multiple-in-multiple-out (MIMO) digital signal processing.

On-chip mode-division multiplexing (MDM) has been emerging as a promising technology to further enhance the link capacity and bandwidth of data communications with multiple mode channels. Both mode converters and mode exchangers are indispensable fundamental components for flexible mode operations. While several configurations have been developed previously, it is still very challenging to efficiently manipulate arbitrary high-order modes in a versatile way to reduce the R&D and prototyping costs. Here we initiate a breakthrough with a simple yet universal generic mode operator building block concept utilizing metasurface structures. The programmable arbitrary high-order mode operators can realize mode conversion and exchange simultaneously with competitive and uniform performance, high stability and compact footprints, offering a quintessential step change for on-chip multimode optical interconnections.

Optical coherence tomography (OCT) is a volumetric imaging modality that empowers clinicians and scientists to noninvasively visualize the cross-sections of biological samples. As the latest generation of its kind, Fourier-domain OCT (FD-OCT) offers a micrometer-scale axial resolution by taking advantage of coherence gating. Based on the current theory, it is believed the only way to obtain a higher-axial-resolution OCT image is to physically extend the system's spectral bandwidth given a certain central wavelength. Here, we showed the belief is wrong. We proposed a novel reconstruction framework, which integrates prior knowledge and exploits the \emph{shift-variance}, to retrospectively super-resolve OCT images without altering the system configurations. Both numerical and experimental results confirmed the processed image manifested an axial resolution beyond the previous theoretical prediction. We believe this result not only opens new horizons for future research directions in OCT reconstruction but also promises an immediate upgrade to tens of thousands of legacy OCT units currently deployed., Comment: 30 pages, 9 figures

Mode-division multiplexing offers a promising solution to increase the data capacity for optical communications. Waveguide mode conversion is essential for on-chip mode-division multiplexing. Previously reported mode converters have been limited to the conversion from a fundamental mode to one particular high-order mode. It is challenging to simultaneously satisfy the phase matching conditions during multiple mode conversion processes. Here, we propose a scalable design method that overcomes this limitation and realizes the simultaneous conversion of multiple modes via an all-dielectric two-dimensional metastructure on a silicon waveguide by shallow etching with hexagonal patterns. As an example, we experimentally demonstrate a multi-mode converter that simultaneously converts the TEi modes to the TEi+3 (i = 0, 1, 2) modes. The length of the multi-mode converter is 16.2 micron. The TE0-TE3, TE1-TE4, and TE2-TE5 mode conversions exhibit low insertion losses (0.4 to 1.0 dB) and reasonable crosstalk values (-14.1 to -16.5 dB) at 1538 nm., Comment: 27 pages, 7 figures

Neuromorphic photonics that aims to process and store information simultaneously like human brains has emerged as a promising alternative for the next generation intelligent computing systems. The implementation of hardware emulating the basic functionality of neurons and synapses is the fundamental work in this field. However, previously proposed optical neurons implemented with SOA-MZIs, modulators, lasers or phase change materials are all dependent on active devices and quite difficult for integration. Meanwhile, although the nonlinearity in nanocavities has long been of interest, the previous theories are intended for specific situations, e.g., self-pulsation in microrings, and there is still a lack of systematic studies in the excitability behavior of the nanocavities including the silicon photonic crystal cavities. Here, we report for the first time a universal coupled mode theory model for all side-coupled passive microresonators. Attributed to the nonlinear excitability, the passive microresonator can function as a new type of all-optical spiking neuron. We demonstrate the microresonator-based neuron can exhibit the three most important characteristics of spiking neurons: excitability threshold, refractory period and cascadability behavior, paving the way to realize all-optical spiking neural networks., Comment: 8 pages, 7 figures

Due to the inherent in-direct bandgap nature of Silicon, heterogeneous integration of semiconductor lasers on Silicon on Insulator (SOI) is crucial for next-generation on-chip optical interconnects. Compact, high-efficient and high-tolerant couplers between III-V light source and silicon chips have been the challenge for photonic integrated circuit (PIC). Here, we redesign the taper adiabatic coupler with the total coupling length of only 4 {\mu}m, and propose another two novel slot coupler and bridge-SWG coupler with both coupling length of 7 {\mu}m, to heterogeneously integrate III-V lasers and silicon chips. We study theoretically the optical mode coupling process through the redesigned taper coupler, the final coupling results match well with the simulation in 3D-FDTD. The three compact couplers represent fundamental TE mode coupling efficiencies all over 90%, even 95.7% for bridge-SWG coupler, to the best of our knowledge, are also the shortest coupling structures (7 um). Moreover, these coupling structures also possess excellent fabrication tolerance., Comment: 7 pages, 7 figures