Reconfigurable multi-channel microwave photonic receiver for a multi-band signal based on cascaded microring resonator banks.

What we believe to be a novel reconfigurable multi-channel microwave photonic (MWP) receiver for multi-band RF signal is demonstrated for the first time, to the best of our knowledge. A reconfigurable MWP signal processing chip based on two cascaded microring filter banks is employed in the proposed receiver, which slices the multi-band RF input into several narrow band signals and selects optical frequency comb lines for frequency converting of each channel. Due to the significant reconfigurability of the signal processing chip, the proposed receiver can flexibly choose the output frequency band of each channel, and thus different frequency components of the multi-band RF input can be down converted to the intermediate frequency (IF) band for receiving or converted to other frequency band for forwarding. A multi-band RF signal composed of a linear frequency modulation (LFM) signal with 2 GHz bandwidth and a quad-phase shift keyed (QPSK) signal with 100 Mbit/s rate is experimentally received and reconstructed by the proposed receiver, where the reconstructed LFM component exhibits a signal to noise ratio (SNR) of 10.2 dB, and the reconstructed QPSK component reaches a high SNR of 26.1 dB and a great error vector magnitude (EVM) of 11.73%. On the other hand, the QPSK component of the multi-band RF signal centered at 13.5 GHz is successfully converted to 3.1 GHz.

High-order LP modes based Sagnac interference for temperature sensing with an enhanced optical Vernier effect.

In this paper, high-order LP modes based Sagnac interference for temperature sensing are proposed and investigated theoretically. Based on the specific high-order LP modes excited through the mode selective couplers (MSCs), we design a stress-induced Panda-type few-mode fiber (FMF) supporting 4 LP modes and construct a Sagnac interferometer to achieve a highly sensitive temperature sensor. The performances of different LP modes (LP01, LP11, LP21, and LP02) are explored under a single Sagnac interferometer and paralleled Sagnac interferometers, respectively. LP21 mode has the highest temperature sensitivity. Compared with fundamental mode (LP01), the temperature sensitivity based on LP21 mode improved by 18.2% at least. In addition, a way to achieve the enhanced optical Vernier effect is proposed. It should be noted that two Sagnac loops are located in two temperature boxes of opposite variation trends, respectively. Both two Sagnac interferometers act as the sensing element, which is different from the traditional optical Vernier effect. The temperature sensitivity of novel enhanced optical Vernier effect is magnified by 8 times, which is larger than 5 times the traditional Vernier effect. The novel approach avoids measurement errors and improves the stability of the sensing system. The focus of this research is on high-order mode interference, which has important guiding significance for the development of highly sensitive Sagnac sensors.

C-DONN: compact diffractive optical neural network with deep learning regression.

A new method to improve the integration level of an on-chip diffractive optical neural network (DONN) is proposed based on a standard silicon-on-insulator (SOI) platform. The metaline, which represents a hidden layer in the integrated on-chip DONN, is composed of subwavelength silica slots, providing a large computation capacity. However, the physical propagation process of light in the subwavelength metalinses generally requires an approximate characterization using slot groups and extra length between adjacent layers, which limits further improvements of the integration of on-chip DONN. In this work, a deep mapping regression model (DMRM) is proposed to characterize the process of light propagation in the metalines. This method improves the integration level of on-chip DONN to over 60,000 and elimnates the need for approximate conditions. Based on this theory, a compact-DONN (C-DONN) is exploited and benchmarked on the Iris plants dataset to verify the performance, yielding a testing accuracy of 93.3%. This method provides a potential solution for future large-scale on-chip integration.

Photonic machine learning with on-chip diffractive optics.

Machine learning technologies have been extensively applied in high-performance information-processing fields. However, the computation rate of existing hardware is severely circumscribed by conventional Von Neumann architecture. Photonic approaches have demonstrated extraordinary potential for executing deep learning processes that involve complex calculations. In this work, an on-chip diffractive optical neural network (DONN) based on a silicon-on-insulator platform is proposed to perform machine learning tasks with high integration and low power consumption characteristics. To validate the proposed DONN, we fabricated 1-hidden-layer and 3-hidden-layer on-chip DONNs with footprints of 0.15 mm2 and 0.3 mm2 and experimentally verified their performance on the classification task of the Iris plants dataset, yielding accuracies of 86.7% and 90%, respectively. Furthermore, a 3-hidden-layer on-chip DONN is fabricated to classify the Modified National Institute of Standards and Technology handwritten digit images. The proposed passive on-chip DONN provides a potential solution for accelerating future artificial intelligence hardware with enhanced performance.

Data-driven fiber model based on the deep neural network with multi-head attention mechanism.

In this paper, we put forward a data-driven fiber model based on the deep neural network with multi-head attention mechanism. This model, which predicts signal evolution through fiber transmission in optical fiber telecommunications, can have advantages in computation time without losing much accuracy compared with conventional split-step fourier method (SSFM). In contrast with other neural network based models, this model obtains a relatively good balance between prediction accuracy and distance generalization especially in cases where higher bit rate and more complicated modulation formats are adopted. By numerically demonstration, this model can have ability of predicting up to 16-QAM 160Gbps signals with any transmission distances ranging from 0 to 100 km under both circumstances of the signals without or with the noise.

Key frames assisted hybrid encoding for high-quality compressive video sensing.

Snapshot compressive imaging (SCI) encodes high-speed scene video into a snapshot measurement and then computationally makes reconstructions, allowing for efficient high-dimensional data acquisition. Numerous algorithms, ranging from regularization-based optimization and deep learning, are being investigated to improve reconstruction quality, but they are still limited by the ill-posed and information-deficient nature of the standard SCI paradigm. To overcome these drawbacks, we propose a new key frames assisted hybrid encoding paradigm for compressive video sensing, termed KH-CVS, that alternatively captures short-exposure key frames without coding and long-exposure encoded compressive frames to jointly reconstruct high-quality video. With the use of optical flow and spatial warping, a deep convolutional neural network framework is constructed to integrate the benefits of these two types of frames. Extensive experiments on both simulations and real data from the prototype we developed verify the superiority of the proposed method.

LOEN: Lensless opto-electronic neural network empowered machine vision.

Machine vision faces bottlenecks in computing power consumption and large amounts of data. Although opto-electronic hybrid neural networks can provide assistance, they usually have complex structures and are highly dependent on a coherent light source; therefore, they are not suitable for natural lighting environment applications. In this paper, we propose a novel lensless opto-electronic neural network architecture for machine vision applications. The architecture optimizes a passive optical mask by means of a task-oriented neural network design, performs the optical convolution calculation operation using the lensless architecture, and reduces the device size and amount of calculation required. We demonstrate the performance of handwritten digit classification tasks with a multiple-kernel mask in which accuracies of as much as 97.21% were achieved. Furthermore, we optimize a large-kernel mask to perform optical encryption for privacy-protecting face recognition, thereby obtaining the same recognition accuracy performance as no-encryption methods. Compared with the random MLS pattern, the recognition accuracy is improved by more than 6%.

On-chip photonic diffractive optical neural network based on a spatial domain electromagnetic propagation model.

An integrated physical diffractive optical neural network (DONN) is proposed based on a standard silicon-on-insulator (SOI) substrate. This DONN has compact structure and can realize the function of machine learning with whole-passive fully-optical manners. The DONN structure is designed by the spatial domain electromagnetic propagation model, and the approximate process of the neuron value mapping is optimized well to guarantee the consistence between the pre-trained neuron value and the SOI integration implementation. This model can better ensure the manufacturability and the scale of the on-chip neural network, which can be used to guide the design and manufacturing of the real chip. The performance of our DONN is numerically demonstrated on the prototypical machine learning task of prediction of coronary heart disease from the UCI Heart Disease Dataset, and accuracy comparable to the state-of-the-art is achieved.

Optical random speckle encoding based on hybrid wavelength and phase modulation.

Optical random speckle encoding suffers from a contradiction between the generation speed and pattern amount. Spatial light modulators are commonly used for random speckle generation at relatively low speeds. Wavelength scanning combined with a scattering medium has a fast speed, while the pattern amount is limited by the optical bandwidth. To increase the performance of optical random speckle encoding, a novel, to the best of our knowledge, scheme combining wavelength and phase hybrid modulation is proposed and demonstrated. Through optical encoding in the two dimensions of wavelength and phase, the number of speckle patterns can reach one million, which is over 10,000 times that generated by only wavelength scanning. This scheme can be used in ghost imaging systems to increase the resolution of reconstructed images.

Modulation bandwidth enhanced self-injection locking laser with an external high-Q microring reflector.

We propose and demonstrate a hybrid modulation bandwidth enhanced self-injection locking laser by butt coupling a commercial distributed feedback laser with an external high-Q silicon nitride microring reflector (MRR). The MRR keeps the laser in strong self-injection locking state with photon-photon resonance, which can realize direct modulation bandwidth enhanced and stable narrow linewidth single-mode output. With the further optimization of MRR parameters, the 3-dB modulation bandwidth and the linewidth of the hybrid laser are enhanced to 15.28 GHz from 7.70 GHz and narrowed to 4 kHz from 600 kHz, respectively. This work makes full use of the advantages of self-injection and integrated photonic technology, which has potential applications in many fields.

Hybrid microwave photonic receiver based on integrated tunable bandpass filters.

Inspired by the concept of system-in-a-package (SiP) in electronics, here we report a hybrid microwave photonic receiver prototype by integrating lithium niobate (LiNbO3) dual-parallel phase modulators with silicon nitride (Si3N4) integrated tunable microring filters. In particular, we experimentally characterize these employed key elements and evaluate the down-conversion performance of RF signals from 4-20 GHz to the intermediate frequency. With the advantages of the tunable microwave photonic signal filtering, uniform system performance within a broad operation bandwidth, and low SWaP, the demonstrated hybrid microwave photonic receiver module shows a potential setup to satisfy the requirements of wireless communication systems, phased-array radar systems, and electronic warfare.

Subwavelength hole defect assisted microring resonator for a compact rectangular filter.

We propose and demonstrate a subwavelength hole defect assisted microring resonator (SHDAMR) structure. With the manipulated modal coupling between two degenerate counterpropagating modes induced by a subwavelength hole defect embedded in the microring waveguide, the SHDAMR structure shows a rectangular resonance lineshape instead of the Lorentzian resonance lineshape of a conventional microring. As a proof of concept, the SHDAMR structure is fabricated on the Si3N4 waveguide platform, for achieving a rectangular filter with a 3-dB bandwidth of 2.03 GHz and an improved shape factor. The demonstrated SHDAMR structure shows the advantages of compact footprint, simplified tunability, and large tolerance of fabrication errors, showing great potential for various applications.

Fast intelligent cell phenotyping for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm.

SIGNIFICANCE: The use of optofluidic time-stretch flow cytometry enables extreme-throughput cell imaging but suffers from the difficulties of capturing and processing a large amount of data. As significant amounts of continuous image data are generated, the images require identification with high speed. AIM: We present an intelligent cell phenotyping framework for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm, which is able to classify obtained cell images rapidly and accurately. The applied image recognition consists of density-based spatial clustering of applications with noise outlier detection, histograms of oriented gradients combining gray histogram fused feature, and XGBoost classification. APPROACH: We tested the ability of this framework against other previously proposed or commonly used algorithms to phenotype two groups of cell images. We quantified their performances with measures of classification ability and computational complexity based on AUC and test runtime. The tested cell image datasets were acquired from high-throughput imaging of over 20,000 drug-treated and untreated cells with an optofluidic time-stretch microscope. RESULTS: The framework we built beats other methods with an accuracy of over 97% and a classification frequency of 3000 cells / s. In addition, we determined the optimal structure of training sets according to model performances under different training set components. CONCLUSIONS: The proposed XGBoost-based framework acts as a promising solution to processing large flow image data. This work provides a foundation for future cell sorting and clinical practice of high-throughput imaging cytometers.

Temperature-insensitive Mach-Zehnder interferometer based on a silicon nitride waveguide platform.

Wavelength shift, caused by temperature fluctuation, critically limits the application of photonic systems. Here, the waveguide geometry is optimized to minimize the wavelength shift due to temperature change and fabrication error. A temperature-insensitive Mach-Zehnder interferometer filter is proposed for a wavelength locker, based on a silicon nitride waveguide. The proposed device achieves a 0.6 pm/K spectral shift over the C-band, which meets the requirements of a wavelength locker for application in dense wavelength division multiplex systems.

Method for suppressing the frequency drift of integrated microwave photonic filters.

The significant frequency drift of integrated microwave photonic filters (IMPFs) is caused by relatively independent frequency fluctuations of the optical carrier and the photonic integrated filter, which imposes a rigid limitation on the practical application. In this paper, a novel method is proposed for suppressing the frequency drift of IMPFs. The scheme is implemented by utilizing an on-chip high-Q microring resonator as a frequency monitoring unit to track the instantaneous frequency drifts caused by the optical carrier drift and the temperature fluctuations of the photonic integrated chip. And the same frequency tuning is simultaneously applied on the photonic integrated filter to suppress the frequency drift of IMPFs based on the differential scheme. As a proof of concept, the proposed IMPF scheme is demonstrated on the Si3N4 platform, and the frequency drift is measured to be tens of MHz in one hour. Compared with conventional IMPF schemes, the frequency drift is significantly suppressed by 86.3% without using complex laser frequency stabilization and temperature control systems.

Single-shot ultrafast phase retrieval photography.

Single-shot ultrafast photography is a powerful tool for science research and industry applications. In this Letter, a novel strategy for ultrafast imaging of two-dimensional complex (amplitude and phase) objects, which is termed single-shot ultrafast phase retrieval photography (SUP), is proposed and demonstrated. The key component of SUP is a silicon photonic integrated chip, which not only has the function of multi-angle illumination, but also provides ultra-short delays for each illumination source. Combined with an ultra-short pulse source and coherent diffraction imaging, SUP can realize ultrafast single-shot imaging. As a proof of concept, the self-developed multiplexed time delay illumination chip was used for experiments, and we demonstrated the reconstructing of a static complex-valued object with a frame sequence depth of 16 frames from single-shot ptychographic data.

Opto-electronic oscillator mediated by acoustic wave in a photonic crystal fiber stimulated in 1 µm band.

An opto-electronic oscillator based on guided acoustic wave Brillouin scattering in a photonic crystal fiber (PCF) stimulated by a light wave in 1 µm band is proposed and demonstrated. A short length of a homemade PCF stimulated by relatively low pump power leads to strong coupling between the pump and probe waves. The oscillation is realized in a feedback loop, in which the acoustic wave bridges the pump and probe. Oscillation is achieved at 1.237 GHz, which matches the resonance of the acoustic mode, in a single-longitudinal-mode operation of the hybrid cavity. It has a high side mode suppression ratio of over 60 dB.

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system.

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.

Spatial-mode-coupling-based dispersion engineering for integrated optical waveguide.

Dispersion ultimately limits the efficiency of the nonlinear process in the optical waveguide. Traditional dispersion engineering method is to tailor the cross-section of the waveguide with both of the height and width. However, the fabrication process limits the design freedom of the height in some cases. To solve the problem, we develop a dispersion engineering technique based on spatial mode coupling. Just by tailoring the width of waveguide without altering the height, the proposed method achieves anomalous dispersion with a range of 70 nm numerically and experimentally changes the dispersion of a micro-ring resonator from -750 ± 30 ps/nm/km to 1300 ± 200 ps/nm/km over a wavelength range of 25 nm with high Q of 0.8 million on the Si3N4/SiO2 waveguide platform. This technique overcomes the restrict from the fabrication process to the optical waveguide on the dispersion control and can enlarge application of the nonlinear optics on chip.

Time-encoded structured illumination microscopy: toward ultrafast superresolution imaging.

An imaging strategy based on optical time-encoded structured illumination microscopy (TE-SIM) opens the way toward ultrafast superresolution imaging. A proof-of-principle experiment is conducted and the introduced TE-SIM accelerates the generation rate of sinusoidal fringe patterns to an unprecedented speed (dozens of megahertz). At such a high speed, superresolution imaging that surpasses the diffraction limit by a factor of 1.4 is demonstrated. This imaging strategy with high temporal and spatial resolution has great potential in many exciting applications, such as dynamic live cell imaging or high-throughput screening.