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Video-based person re-identification (Re-ID) aims at retrieving the person through the video sequences across non-overlapping cameras. Some characteristics of pedestrians are not consecutive across frames due to the variations of viewpoints, postures, and occlusions over time. However, existing methods ignore such data peculiarity and the networks tend to only learn those salient consecutive characteristics among frames in video sequences. As a result, the learned representations fail to cover all the characteristics of pedestrians, thus lacking integrity and discrimination. To tackle this problem, we present a novel deep architecture termed Hierarchical Mining Network (HMN), which mines as many pedestrians’ characteristics by referring to the temporal and intra-class knowledge. It consists of a novel Attentive Temporal Module (ATM) and a Dynamic Supervising Branch (DSB), with a Balancing Triplet Loss (BTL) assisting the training. The proposed ATM, with pedestrian perceiving capacity, is capable of evaluating each activation of features through temporal analysis, so that the temporally scattered characteristics of pedestrians can be better aggregated and the contaminated ones can be eliminated. Then, the DSB along with the BTL further enhances the integrity of representations by multiple supervision. Specifically, the DSB perceives the diversities of intra-class samples in each mini-batch and generates targeted supervising signals for them, in which process the BTL guarantees the signals with smaller intra-class variations and larger inter-class variations. Comprehensive experiments on two video-based datasets, i.e., MARS, and DukeMTMC-VideoReID, demonstrate the contribution of each component and the superiority of the proposed HMN over the state-of-the-arts. Benchmarking our model on three popular image-based datasets, i.e., Market1501, DukeMTMC-Reid, and MSMT17 additionally verifies the promising generalizability of the proposed DSB and BTL.

Recent years have seen many advances based on Deep Convolutional Neural Networks (DCNNs) in the tasks of face recognition, most of which are developed to pursue high recognition accuracy. In this paper, we propose a novel Fast FAce Recognizer (Fast-FAR), learning to improve the speed of DCNN-based face recognition model without sacrificing recognition accuracy. Our fundamental insight is that the computation increases exponentially with the depth of a network, the easily identifiable face images can be accurately recognized by the cheap features (pixel values at shallow layers), while the challenging samples that exhibit low quality, large pose variations or occlusions need to be processed by the expensive deep layers. The major contribution of this paper is the Reinforcement Learning Agent (RLA), which is proposed to learn a decision policy determined by a reward function. The policy adaptively decides whether the recognition should be performed at an early layer with a high recognition confidence, or proceeding to the subsequent layers, thus significantly reducing feed-forward cost for the easy faces. According to the extensive experiments on the popular face recognition benchmarks, Fast-FAR reduces the inference time by 14.22%, 20.61%, and 7.84% on the dataset LFW, AgeDB-30 and CFP-FP, respectively.

3D face reconstruction from a single 2D image is a challenging problem with broad applications. Recent methods typically aim to learn a CNN-based 3D face model that regresses coefficients of 3D Morphable Model (3DMM) from 2D images to render 3D face reconstruction or dense face alignment. However, the shortage of training data with 3D annotations considerably limits performance of those methods. To alleviate this issue, we propose a novel 2D-assisted self-supervised learning (2DASL) method that can effectively use "in-the-wild" 2D face images with noisy landmark information to substantially improve 3D face model learning. Specifically, taking the sparse 2D facial landmarks as additional information, 2DSAL introduces four novel self-supervision schemes that view the 2D landmark and 3D landmark prediction as a self-mapping process, including the 2D and 3D landmark self-prediction consistency, cycle-consistency over the 2D landmark prediction and self-critic over the predicted 3DMM coefficients based on landmark predictions. Using these four self-supervision schemes, the 2DASL method significantly relieves demands on the the conventional paired 2D-to-3D annotations and gives much higher-quality 3D face models without requiring any additional 3D annotations. Experiments on multiple challenging datasets show that our method outperforms state-of-the-arts for both 3D face reconstruction and dense face alignment by a large margin., 10 pages, 7figures, 3 tables

Face anti-spoofing aims to detect presentation attack to face recognition--based authentication systems. It has drawn growing attention due to the high security demand. The widely adopted CNN-based methods usually well recognize the spoofing faces when training and testing spoofing samples display similar patterns, but their performance would drop drastically on testing spoofing faces of novel patterns or unseen scenes, leading to poor generalization performance. Furthermore, almost all current methods treat face anti-spoofing as a prior step to face recognition, which prolongs the response time and makes face authentication inefficient. In this article, we try to boost the generalizability and applicability of face anti-spoofing methods by designing a new generalizable face authentication CNN (GFA-CNN) model with three novelties. First, GFA-CNN introduces a simple yet effective total pairwise confusion loss for CNN training that properly balances contributions of all spoofing patterns for recognizing the spoofing faces. Second, it incorporate a fast domain adaptation component to alleviate negative effects brought by domain variation. Third, it deploys filter diversification learning to make the learned representations more adaptable to new scenes. In addition, the proposed GFA-CNN works in a multi-task manner—it performs face anti-spoofing and face recognition simultaneously. Experimental results on five popular face anti-spoofing and face recognition benchmarks show that GFA-CNN outperforms previous face anti-spoofing methods on cross-test protocols significantly and also well preserves the identity information of input face images.

Reduced graphene oxide has attracted numerous interests due to its unique, superior electronic, optical, mechanical, and chemical properties. An epoxy resin with excellent mechanical and electrical properties can be obtained by doping with reduced graphene oxide to enhance the function of the polymer. Here, we prepared a uniform reduced graphene oxide/epoxy resin coating with a different reduced graphene oxide content and characterized it using a field-emission scanning electron microscope (FE-SEM), X-ray diffractometer (XRD), Raman, and Fourier transform infrared spectrometer (FTIR). Furthermore, the spectral characteristics of the composite coating in the terahertz band were discussed. The cross-sectional SEM results show that a fold structure with ductile failure was intensively formed due to the compatibility of graphene and polymer materials. Both the Raman G and Raman 2D peaks of reduced graphene oxide were confirmed using Raman spectrum testing. The diffraction peak of reduced graphene oxide at 24° disappeared within the reduced graphene oxide/epoxy resin coating, and a wide diffraction peak of the amorphous structure was formed together. Additionally, the intensity of the Raman spectrum increased significantly with increased reduced graphene oxide content, thereby making the surface electrical resistance of the coatings decrease exponentially. Additionally, the intensity of the terahertz time-domain signal and frequency-domain power spectrum linearly reduced with increased reduced graphene oxide concentration. However, the terahertz absorption coefficient and refractive index both increased gradually with increased reduced graphene oxide doping due to increased orientation polarization in the composite coating.

We present a versatile model, FaceAnime, for various video generation tasks from still images. Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks. However, the generated face images usually suffer from quality loss, image distortion, identity change, and expression mismatching due to the weak representation capacity of the facial landmarks. In this paper, we propose to "imagine" a face video from a single face image according to the reconstructed 3D face dynamics, aiming to generate a realistic and identity-preserving face video, with precisely predicted pose and facial expression. The 3D dynamics reveal changes of the facial expression and motion, and can serve as a strong prior knowledge for guiding highly realistic face video generation. In particular, we explore face video prediction and exploit a well-designed 3D dynamic prediction network to predict a 3D dynamic sequence for a single face image. The 3D dynamics are then further rendered by the sparse texture mapping algorithm to recover structural details and sparse textures for generating face frames. Our model is versatile for various AR/VR and entertainment applications, such as face video retargeting and face video prediction. Superior experimental results have well demonstrated its effectiveness in generating high-fidelity, identity-preserving, and visually pleasant face video clips from a single source face image.

In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination,low resolution, blur and noise. To address these challenges, previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. However, most of these methods are stage-wise, which is sub-optimal and deviates from the reality. In this paper, we address all these challenges jointly for unconstrained face recognition. We propose an Multi-Degradation Face Restoration (MDFR) model to restore frontalized high-quality faces from the given low-quality ones under arbitrary facial poses, with three distinct novelties. First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart. Second, MDFR introduces a pose residual learning strategy along with a 3D-based Pose Normalization Module (PNM), which can perceive the pose gap between the input initial pose and its real-frontal pose to guide the face frontalization. Finally, MDFR can generate frontalized high-quality face images by a single unified network, showing a strong capability of preserving face identity. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of MDFR over state-of-the-art methods on both face frontalization and face restoration., 14 pages, 9 figures

Deep Convolutional Neural Networks (DCNNs) have achieved impressive performance in Single Image Super-Resolution (SISR). To further improve the performance, existing CNN-based methods generally focus on designing deeper architecture of the network. However, we argue blindly increasing network's depth is not the most sensible way. In this paper, we propose a novel end-to-end Residual Neuron Attention Networks (RNAN) for more efficient and effective SISR. Structurally, our RNAN is a sequential integration of the well-designed Global Context-enhanced Residual Groups (GCRGs), which extracts super-resolved features from coarse to fine. Our GCRG is designed with two novelties. Firstly, the Residual Neuron Attention (RNA) mechanism is proposed in each block of GCRG to reveal the relevance of neurons for better feature representation. Furthermore, the Global Context (GC) block is embedded into RNAN at the end of each GCRG for effectively modeling the global contextual information. Experiments results demonstrate that our RNAN achieves the comparable results with state-of-the-art methods in terms of both quantitative metrics and visual quality, however, with simplified network architecture., Comment: 6 pages, 4 figures, Accepted by IEEE ICIP 2020

Supervoxel segmentation has become an essential tool to medical image analysis for three-dimension MR image. However, in no consideration of the intensity inhomogeneity in 2D/3D MR image, the state-of-the-art supervoxel segmentation methods do not satisfy the further analysis, such as tissue classification according to intensity feature. In order to overcome the above-mentioned issues, we propose a modified supervoxel segmentation method for three-dimension MR image, which integrates the bias field into the weighted distance metric to determine the nearest cluster center. The supervoxel segmentation and bias correction can be simultaneously completed in our method. Especially, the bias corrected image lays the foundation for the supervoxel classification in accordance with the intensity feature. The experimental results and quantitative evaluation showed that the supervoxels obtained by our method are adherence to the MR tissue boundaries, and the bias corrected image is positive for the intensity feature extraction.

In this paper, we address the challenging unconstrained set-based face recognition problem where each subject face is instantiated by a set of media (images and videos) instead of a single image. Naively aggregating information from all the media within a set would suffer from the large intra-set variance caused by heterogeneous factors (e.g., varying media modalities, poses and illumination) and fail to learn discriminative face representations. A novel Multi-Prototype Network (MP- Net) model is thus proposed to learn multiple prototype face representations adaptively from the media sets. Each learned prototype is representative for the subject face under certain condition in terms of pose, illumination and media modality. Instead of handcrafting the set partition for prototype learn- ing, MPNet introduces a Dense SubGraph (DSG) learning sub-net that implicitly untangles inconsistent media and learns a number of representative prototypes. Qualitative and quantitative experiments clearly demonstrate the superiority of the proposed model over state-of-the-arts.

3D face reconstruction from a single 2D image is a very important topic in computer vision. However, the current reconstruction methods are usually non-sensitive to face identities and over-sensitive to facial poses, which may result in similar 3D geometries for faces of different identities, or obtain different shapes for the same identity with different poses. When such methods are applied practically, their 3D estimates are either changeable for different photos of the same subject or over-regularized and generic to distinguish face identities. In this paper, we propose a robust solution to solve this problem by carefully designing a novel Siamese Convolutional Neural Network (SCNN). Specifically, regarding the 3D Morphable face Model (3DMM) parameters of the same individual as the same class, we employ the contrastive loss to enlarge the inter-class distance and meanwhile reduce the intra-class distance for the output 3DMM parameters. We also propose an identity loss to preserve the identity information for the same individual in the feature space. Training with these two losses, our SCNN could learn representations that are more discriminative for face identity and generalizable for pose variants. Experiments on the challenging database 300W-LP and AFLW2000-3D have shown the effectiveness of our method by comparing with state-of-the-arts., 5 pages, 6 figures, IEEE International Conference on Information Communication and Signal Processing

Manipulating human facial images between two domains is an important and interesting problem in computer vision. Most of the existing methods address this issue by applying two generators or one generator with extra conditional inputs to generate face images with manipulated attribute. In this paper, we proposed a novel self-perception method based on Generative Adversarial Networks (GANs) for automatic face attribute inverse, where giving a face image with an arbitrary facial attribute the model can generate a new face image with the reversed facial attribute. The proposed method takes face images as inputs and employs only one single generator without being conditioned on other inputs. Profiting from the multi-loss strategy and modified U-net structure, our model is quite stable in training and capable of preserving finer details of the original face images. The extensive experimental results have demonstrated the effectiveness of our method on generating high-quality and realistic attribute-reversed face images.

MicroRNAs have been identified as promising biomarkers for human diseases. The development of a point-of-care (POC) test for the disease-associated miRNAs would be especially beneficial, since miRNAs are unexpectedly well preserved in various human specimens, including urine. Here, we present the Mach-Zehnder interferometer-miRNA detection system capable of detecting multiple miRNAs in clinical urine samples rapidly and simultaneously in a label-free and real-time manner. Through measurement of the light phase change, the MZI sensor provides an optical platform for fast profiling of small molecules with improved accuracy. We demonstrate that this system could specifically detect target miRNAs (miR-21, and let-7a), and even identify the single nucleotide polymorphism of the let-7 family of miRNAs from synthetic and cell line samples. The clinical applicability of this system is confirmed by simultaneously detecting two types of miRNAs in urine samples of bladder cancer patients in a single reaction, with a detection time of 15 min. The POC system can be expanded to detect a number of miRNAs of different species and should be useful for a variety of clinical applications requiring at or near the site of patient care.

Previous approaches for scene text detection usually rely on manually defined sliding windows. This work presents an intuitive two-stage region-based method to detect multi-oriented text without any prior knowledge regarding the textual shape. In the first stage, we estimate the possible locations of text instances by detecting and linking corners instead of shifting a set of default anchors. The quadrilateral proposals are geometry adaptive, which allows our method to cope with various text aspect ratios and orientations. In the second stage, we design a new pooling layer named Dual-RoI Pooling which embeds data augmentation inside the region-wise subnetwork for more robust classification and regression over these proposals. Experimental results on public benchmarks confirm that the proposed method is capable of achieving comparable performance with state-of-the-art methods. The code is publicly available at https://github.com/xhzdeng/crpn .

We present a computer-aided diagnosis system (CADx) for the automatic categorization of solid, part-solid and non-solid nodules in pulmonary computerized tomography images using a Convolutional Neural Network (CNN). Provided with only a two-dimensional region of interest (ROI) surrounding each nodule, our CNN automatically reasons from image context to discover informative computational features. As a result, no image segmentation processing is needed for further analysis of nodule attenuation, allowing our system to avoid potential errors caused by inaccurate image processing. We implemented two computerized texture analysis schemes, classification and regression, to automatically categorize solid, part-solid and non-solid nodules in CT scans, with hierarchical features in each case learned directly by the CNN model. To show the effectiveness of our CNN-based CADx, an established method based on histogram analysis (HIST) was implemented for comparison. The experimental results show significant performance improvement by the CNN model over HIST in both classification and regression tasks, yielding nodule classification and rating performance concordant with those of practicing radiologists. Adoption of CNN-based CADx systems may reduce the inter-observer variation among screening radiologists and provide a quantitative reference for further nodule analysis.

A practical face recognition system demands not only high recognition performance, but also the capability of detecting spoofing attacks. While emerging approaches of face anti-spoofing have been proposed in recent years, most of them do not generalize well to new database. The generalization ability of face anti-spoofing needs to be significantly improved before they can be adopted by practical application systems. The main reason for the poor generalization of current approaches is the variety of materials among the spoofing devices. As the attacks are produced by putting a spoofing display (e.t., paper, electronic screen, forged mask) in front of a camera, the variety of spoofing materials can make the spoofing attacks quite different. Furthermore, the background/lighting condition of a new environment can make both the real accesses and spoofing attacks different. Another reason for the poor generalization is that limited labeled data is available for training in face anti-spoofing. In this paper, we focus on improving the generalization ability across different kinds of datasets. We propose a CNN framework using sparsely labeled data from the target domain to learn features that are invariant across domains for face anti-spoofing. Experiments on public-domain face spoofing databases show that the proposed method significantly improve the cross-dataset testing performance only with a small number of labeled samples from the target domain., Comment: 8 pages; 3 figures; 2 tables

We demonstrate a highly sensitive label-free Mach–Zehnder interferometer (MZI) biosensor based on silicon nitride slot waveguide. Unlike the conventional MZI sensors, the sensing arm of the sensor consists of a slot waveguide while the reference arm consists of a strip waveguide. Thanks to the slot waveguide's property to provide high optical intensity in a subwavelength-size low refractive index region (slot region), which allows high light–analyte interaction, higher sensitivity can be obtained as compared to conventional waveguides using the slot waveguide as sensing region. The bulk refractive index sensitivity of the slot waveguide MZI sensor was found to be 1864π/RIU (refractive index unit) with 7 mm long slot waveguide sensing arm, which shows higher sensitivity compared to the conventional MZI device based on silicon nitride. The biosensing capability of the developed slot waveguide MZI was investigated using biotin–streptavidin binding as a model system. The sensitivity of the system was demonstrated down to 18.9 fM or 1 pg/ml of streptavidin solution and to the best of our knowledge, it is the best reported experimental value for the limit of detection of a MZI sensor. Furthermore, we investigated the specific detection and quantification of the methylation of DAPK (Death-associated protein kinase) gene, which is a widely used biomarker for human cancers. We have shown that methylation sequences of DAPK gene of various methylation densities (100%, 50%, and 0% of methylation sites) can be quantified and discriminated even at a concentration as low as 1 fmol/μl or 1 nM. ASTAR (Agency for Sci., Tech. and Research, S’pore)

Silicon photonics integrated circuits (Si-PIC) with well-established active and passive building elements are progressing towards large-scale commercialization in optical communications and high speed optical interconnects applications. However, current Si-PICs do not have memory capabilities, in particular, the non-volatile memory functionality for energy efficient data storage. Here, we propose an electrically programmable, multi-level non-volatile photonics memory cell (PMC) fabricated by standard complementary-metal-oxide-semiconductor (CMOS) compatible processes. A micro-ring resonator (MRR) was built using the PMC to optically read the memory states. Switching energy smaller than 20 pJ was achieved. Additionally, a MRR memory array was employed to demonstrate a four-bit memory read capacity. Theoretically, this can be increased up to ~400 times using a 100 nm free spectral range broadband light source. The fundamental concept of this design provides a route to eliminate the von Neumann bottleneck. The energy-efficient optical storage can complement on-chip optical interconnects for neutral networking, memory input/output interfaces and other computational intensive applications.

The first pn-type carrier-induced silicon Bragg-grating filter is demonstrated. The extinction-ratio modulations are 11.5 dB and 10 dB with reverse and forward biases, respectively. 8-Gpbs data rate is achieved with a reverse bias., {"references":["H. Tsuda, \"Fiber Bragg grating vibration-sensing system, insensitive to Bragg wavelength and employing fiber ring laser,\" Opt. Lett. 35, pp. 2349-2351 (2010).","Y. G. Han and S. B. Lee, \"Tunable dispersion compensator based on uniform fiber Bragg grating and its application to tunable pulse repetition-rate multiplication,\" Opt. Express 13, pp. 9224-9229 (2005).","D. T. H. Tan, et al., \"Cladding-modulated Bragg grating in silicon waveguides,\" Opt. Lett. 34, pp. 1357-1359 (2006).","S. Honda, et al., \"Largely-tunable wideband Bragg gratings fabricated on SOI rib waveguides employed by deep-RIE,\" Electron. Lett. 43, pp. 630-631 (2007).","G. Jiang, et al., \"Slab-modulated sidewall Bragg grating in silicon-on-insulator ridge waveguides,\" IEEE Photon. Technol. 23, pp. 6-8 (2011).","Q. Fang, et al., \"Carrier-induced silicon Bragg grating filters with a p-i-n junction,\" IEEE Photon. Technol. 25, pp. 810-812 (2013)."]}

We demonstrate a high-efficiency and CMOS-compatible silicon Mach-Zehnder Interferometer (MZI) optical modulator with Cu traveling-wave electrode and doping compensation. The measured electro-optic bandwidth at Vbias = -5 V is above 30 GHz when it is operated at 1550 nm. At a data rate of 50 Gbps, the dynamic extinction ratio is more than 7 dB. The phase shifter is composed of a 3 mm-long reverse-biased PN junction with modulation efficiency (Vπ·Lπ) of ~18.5 V·mm. Such a Cu-photonics technology provides an attractive potentiality for integration development of silicon photonics and CMOS circuits on SOI wafer in the future.

We proposed a microring resonator (MRR) enhanced photodetector (PD) structure. Resonance wavelength enhanced by the MRR amplifies the PD response. At L-band wavelengths, responsivity was doubled for an ultra-short germanium PD of 4 µm employing the MRR structure. Data rates of up to 40 Gb/s were also demonstrated at 1600 nm.

We demonstrate silicon-based traveling-wave photodetector arrays (Si-TWPDAs) with parallel optical feeding by integrating multiple Germanium photodetectors. Such Si-TWPDAs feature the merit of high optical saturation power with remaining the large operation bandwidth. The impedance-matched traveling-wave electrode design takes into account the individual Ge photodetector loading effect. Optical waveguide delay lines are designed in order to balance the electrical phase delay of the traveling-wave electrode. The maximum linear photocurrent at −4V biased voltage are respectively 16 mA, 38 mA, and 65 mA with integrating 1, 2, and 4 photodetectors, upon the saturation power of 40 mW, 100 mW, and 160 mW. This corresponds to a normalized photocurrent generation of >0.32 mA/µm3 and a normalized saturation power of 0.8 mW/µm3. The extracted fiber access responsivity is ~0.42 A/W and the intrinsic responsivity of ~0.82 A/W. The measured 3-dB bandwidth for 4-channel TWPDA is ~20 GHz.

A low-loss high-speed silicon in-phase (I) quadrature (Q) modulator is designed, fabricated and characterized. The fabricated IQ modulator has a low passive optical loss of 9 dB in C and L bands. Using the modulator, differential quadrature phase-shift keying (DQPSK) transmission at 44.6 Gb/s with differential detection is confirmed with an optical signal-to-noise ratio (OSNR) of 16.3 dB for a bit error rate (BER) of 10(-3) and a dispersion tolerance of -96 to 107 ps/nm. Moreover, in digital coherent detection, quadrature phase-shift keying (QPSK) up to 64 Gb/s are achieved with an OSNR of 11.6-11.8 dB for a BER of 10(-2) at 1530, 1550, and 1610 nm.

The wavelength band near 1300 nm is attractive for many telecommunications applications, yet there are few results in silicon that demonstrate high-speed modulation in this band. We present the first silicon modulator to operate at 50 Gbps near 1300 nm. We demonstrate an open eye at this speed using a differential 1.5 V(pp) signal at 0 V reverse bias, achieving an energy efficiency of 450 fJ/bit.

We investigate the performances of a silicon PN-junction Mach-Zehnder modulator for analog application. The slope efficiency and spurious-free dynamic range (SFDR) of such a modulator upon carrier depletion and carrier injection effects are characterized and compared. Input RF frequency-dependence measurements show that the depletion-type modulator is usually with ~20 dB ∙ Hz(2/3) higher SFDR comparing to the injection-type modulator, yet with an order-of-magnitude lower slope efficiency. For the depletion-type and injection-type modulators, the measured maximum SFDRs are respectively ~95 dB ∙ Hz(2/3) and 75 dB∙Hz(2/3), with maximum slope efficiency of 0.3 V(-1) and 8 V(-1

We demonstrate silicon Mach-Zehnder Interferometer (MZI) optical modulator with 50.1-Gb/s data rate and 5.56 dB dynamic extinction ratios. The phase shifter is composed by a 4 mm-long reverse-biased p-n junction with a modulation efficiency (V(π) · L(π)) of ~26.7 V · mm and phase shifter loss of ~1.04 dB/mm at V(bias) = -6 V. The measured electro-optic bandwidth reaches 25.6 GHz at V(bias) = -5 V. Compensation doping method and low loss traveling-wave electrodes are utilized to improve the modulator performance. Measurement result demonstrates that reasonable choosing of working point and doping profile of the silicon optical modulator is critical in order to match the performance requirement of the real application.

Conventional silicon photonic integrated circuits do not normally possess memory functions, which require on-chip power in order to maintain circuit states in tuned or field-configured switching routes. In this context, we present an electrically programmable add/drop microring resonator with a wavelength shift of 426 pm between the ON/OFF states. Electrical pulses are used to control the choice of the state. Our experimental results show a wavelength shift of 2.8 pm/ms and a light intensity variation of ~0.12 dB/ms for a fixed wavelength in the OFF state. Theoretically, our device can accommodate up to 65 states of multi-level memory functions. Such memory functions can be integrated into wavelength division mutiplexing (WDM) filters and applied to optical routers and computing architectures fulfilling large data downloading demands.

We demonstrate a 1×4 coarse wavelength division-multiplexing (CWDM) planar concave grating multiplexer/demultiplexer and its application in re-configurable optical add/drop multiplexer (ROADM) system in silicon-on-insulator substrate. The wavelengths of the demonstrated concave grating multiplexer align well with the ITU-T standard. We demonstrate a prototype of ROADM comprising two such concave gratings and four wide-band thermo-optical MZI switches. Undercut technology which removes the underneath silicon substrate is adopted in optical switches in order to minimize the operation power. For all the thermal heaters, the operation voltage is smaller than 1.5 V, and the switch power is ~2.4 mW. High throughput pseudorandom binary sequence (PRBS) data transmission with up to 100 Gb/s is demonstrated, showing the high-performance ROADM functionality.

As the sensitivity and detection limit of photonic refractive index (RI) biosensor increases, the temperature dependence becomes a major challenge. In this paper, we present a Mach-Zehnder Interferometer (MZI) biosensor based on silicon nitride slot waveguides. The biosensor is designed for minimal temperature dependence without compromising the performance in terms of sensitivity and detection limit. With air cladding, the measured surface sensitivity and detection limit of MZI biosensor reach 7.16 nm/(ng mm(-2)) and 1.30 (pg mm(-2)), while achieving a low temperature dependence is 5.0 pm/° C. With water cladding, the measured bulk sensitivity and detection limit reach 1730(2π)/RIU and 1.29 × 10(-5) RIU respectively. By utilizing Vernier effect through cascaded MZI structures, the measured sensitivity enhancement factor is 8.38, which results in a surface detection limit of 0.155 (pg mm(-2)).

Silicon Mach-Zehnder modulators with reduced series resistance in lateral PN junction rib-waveguide phase shifters for enhanced high-speed response are fabricated and characterized. Extinction ratio higher than 10 dB is obtained at 10.3-11.7 Gbps with mask margins of 27% (10.3-Gbps 10GBE), 16% (10.7-Gbps STM-64/OC-192) and 10% (11.3-Gbps STM-64/OC-192) in eye-diagram measurements incorporating mask tests using a RF cut-off filter. In unfiltered eye-diagram measurements without mask tests, extinction ratio higher than 13 dB is obtained at 10.0-12.5 Gbps. The silicon modulators reveal high-speed performance comparable with that of lithium-niobate modulators in high-speed optical fiber telecommunications.

Compared with an optical modulator based on lithium niobate, the total loss of the current high speed silicon modulator is still too high for commercial use. Reduction of the total loss always comes along with the degradation of the other two characteristics including modulation efficiency or switching speed. In this paper, we reduce the phase shifter loss through optimizing the doping level out of the depletion region while keeping the modulation efficiency and switching speed at a high level. Compensated doping method is utilized to optimize the doping level on the cross section of the phase shift. With doping compensation, the Loss·Efficiency figure-of-merit (FOM) of 4 mm phase shifter is reduced from 25.8 dB·V to 19.4 dB·V while still keeping the small signal 3 dB-bandwidth at about 10 GHz. After doping profile optimizing, the measured bandwidth of the phase shifter with doping compensation can even reaches 17 GHz with a Loss·Efficiency FOM of about 25.4 dB·V.

Inversely tapered spot size converter (SSC) is widely used to connect silicon waveguide with fiber in silicon photonics. However, the tapered structure may cause polarization rotation and further generate interference fluctuation in the transmission spectrum even of a straight waveguide. We analyzed the light propagation in a straight waveguide with SSC at the both ends with coupling matrix and transmission matrix methods. The analysis results matched with the phenomena we observed in the transmission spectrum. Combining the analysis with the measurement results, we calculated the polarization rotation efficiency of the SSC in different samples and analyzed the origin of the polarization rotation effect. Finally, we discussed the influence of the effect to the DP-QPSK signal and proposed several methods to release the impact.

In this study, slot waveguide ring resonators patterned on a silicon-on-insulator (SOI) wafer and coated with an atomic layer deposited nanolaminate consisting of alternating layers of tantalum pentoxide and polyimide were fabricated and characterized. To the best of our knowledge, this is the first demonstration of atomic layer deposition (ALD) of organic materials in waveguiding applications. In our nanolaminate ring resonators, the optical power is not only confined in the narrow central air slot but also in several parallel sub-10 nm wide vertical polyimide slots. This indicates that the mode profiles in the silicon slot waveguide can be accurately tuned by the ALD method. Our results show that ALD of organic and inorganic materials can be combined with conventional silicon waveguide fabrication techniques to create slot waveguide ring resonators with varying mode profiles. This can potentially open new possibilities for various photonic applications, such as optical sensing and all-optical signal processing.

We demonstrate the first PN-type carrier-induced silicon waveguide Bragg grating filter on a SOI wafer. The optical extinction ratio of this kind of filter can be efficiently modulated under both reverse and forward biases. The carrier-induced Bragg grating based on a PN junction is fabricated on the silicon waveguide using litho compensation technology. The measured optical bandwidth and the extinction ratio of the filter are 0.45 nm and 19 dB, respectively. The optical extinction ratio modulation under the reverse bias is more than 11.5 dB and it is more than 10 dB under the forward bias. Only 1-dB optical transmission loss is realized in this Bragg grating under a reverse bias. The shifting rates of the central wavelength under forward and reverse biases are ~-1.25 nm/V and 0.01 nm/V, respectively. The 3-dB modulation bandwidth of this filter is 5.1 GHz at a bias of -10 V.

We propose a novel coupled resonator optical waveguide (CROW) structure that is made up of a waveguide loop. We theoretically investigate the forbidden band and conduction band conditions in an infinite periodic lattice. We also discuss the reflection- and transmission- spectra, group delay in finite periodic structures. Light has a larger group delay at the band edge in a periodic structure. The flat band pass filter and flat-top group delay can be realized in a non-periodic structure. Scattering matrix method is used to calculate the effects of waveguide loss on the optical characteristics of these structures. We also introduce a tunable coupling loop waveguide to compensate for the fabrication variations since the coupling coefficient of the directional coupler in the loop waveguide is a critical factor in determining the characteristics of a loop CROW. The loop CROW structure is suitable for a wide range of applications such as band pass filters, high Q microcavity, and optical buffers and so on.

High-index-contrast (HIC) waveguide such as Si and Si3N4 has small mode size enabling compact integration. However, the coupling loss with single mode fiber is also remarkable owning to the mode mismatching. Therefore, mode size converter, as the interface between HIC waveguide and optical fiber, takes an important role in the field of integrated optics. The material with refractive index (RI) between HIC waveguide and optical fiber can be used as a bridge to reduce the mode mismatching loss. In this letter, we employ silicon oxynitride (SiON) with RI about 1.50 as the intermediate material and optimize the structure of the SiON waveguide to match with cleaved single mode fiber and HIC waveguide separately. Combined with inverse taper and suspended structure, the mismatching loss is reduced and the dependence to the dimension of the structure is also released. The coupling loss is 1.2 and 1.4 dB/facet for TE and TM mode, respectively, with 3 dB alignment tolerance of ± 3.5 μm for Si(3)N(4) waveguide with just 200 nm-wide tip. While for Si waveguide, a critical dimension of 150 nm is applied due to the higher index contrast than Si(3)N(4) waveguide. Similar alignment tolerance is realized with coupling loss about 1.8 and 2.1 dB/facet for TE and TM mode. The polarization dependence loss (PDL) for both platforms is within 0.5 dB.

A silicon Mach-Zehnder Interferometer (MZI) optical modulator with a shield coplanar waveguide (CPW) transmission line electrode design was demonstrated. This shield-CPW electrode suppresses the signal distortion caused by the parasitic slot-line (SL) mode and improves the electrical bandwidth and the electro-optical (EO) bandwidth. With the shield-CPW electrodes and 5.5 mm-long phase shifters, the silicon MZI optical modulator delivered an EO bandwidth of above 24 GHz and a V (π) = 3.0 V was achieved at λ = 1310 nm. When modulated at 28-Gb/s data rate, it achieved an extinction ratio of 5.66 dB under a driving voltage of V (pp) = 1.3 V, corresponding to a power consumption of 0.8 pJ/bit.

In this paper we have designed, fabricated and characterized a high efficiency Silicon nitride grating coupler at 1490 nm. Distributed Bragg reflectors as bottom mirrors are employed to improve the coupling efficiency by reflecting the downward traveling light. The peak coupling efficiency obtained is about -2.5 dB and the 1-dB bandwidth is 53 nm. The fabrication process is CMOS-compatible and is ready to be integrated with photonic circuits.

We propose a novel three-dimensional (3D) monolithic optoelectronic integration platform. Such platform integrates both electrical and photonic devices in a bulk silicon wafer, which eliminates the high-cost silicon-on-insulator (SOI) wafer and is more suitable for process requirements of electronic and photonic integrated circuits (ICs). For proof-of-concept, we demonstrate a three-dimensional photodetector and WDM receiver system. The Ge is grown on a 8-inch bulk silicon wafer while the optical waveguide is defined in a SiN layer which is deposited on top of it, with ~4 µm oxide sandwiched in between. The light is directed to the Ge photodetector from the SiN waveguide vertically by using grating coupler with a Aluminum mirror on top of it. The measured photodetector responsivity is ~0.2 A/W and the 3-dB bandwidth is ~2 GHz. Using such vertical-coupled photodetector, we demonstrated an 8-channel receiver by integrating a 1 × 8 arrayed waveguide grating (AWG). High-quality optical signal detection with up to 10 Gbit/s data rate is demonstrated, suggesting a 80 Gbit/s throughput. Such receiver can be applied to on-chip optical interconnect, DRAM interface, and telecommunication systems.