Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics.

Analog feature extraction (AFE) is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled data. However, applying AFE to broadband radio-frequency (RF) scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic circuitry. Here, we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog domain. The feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas, extracting valid features from both temporal and spatial dimensions. Because of the tunability of the photonic devices, the photonic spatiotemporal feature extractor is trainable, which enhances the validity of the extracted features. Moreover, a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature extractor. To validate our scheme, we demonstrate a radar target recognition task with a 4-GHz instantaneous bandwidth. Experimental results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%. Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing, with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving, robotics, and smart factories.

Effect of C Addition on the Microstructure and Fracture Properties of In Situ Laminated Nb/Nb5Si3 Composites.

//Nbss and α-Nb5Si3 phases were detected. Meanwhile, Nb2C was observed, and the crystal forms of Nb5Si3 changed in the C-doped composites. Furthermore, micron-sized and nano-sized Nb2C particles were found in the Nbss layer. The orientation relationship of Nb2C phase and the surrounding Nbss was [001]Nbss//[010]Nb2C, (200) Nbss//(101) Nb2C. Additionally, with the addition of C, the compressive strength of the composites, at 1400 °C, and the fracture toughness increased from 310 MPa and 11.9 MPa·m1/2 to 330 MPa and 14.2 MPa·m1/2, respectively; the addition of C mainly resulted in solid solution strengthening.

Low-threshold all-optical nonlinear activation function based on injection locking in distributed feedback laser diodes.

We experimentally demonstrate an all-optical nonlinear activation unit based on the injection-locking effect of distributed feedback laser diodes (DFB-LDs). The nonlinear carrier dynamics in the unit generates a low-threshold nonlinear activation function with optimized operating conditions. The unit can operate at a low threshold of -15.86 dBm and a high speed of 1 GHz, making it competitive among existing optical nonlinear activation approaches. We apply the unit to a neural network task of solving the second-order ordinary differential equation. The fitting error is as low as 0.0034, verifying the feasibility of our optical nonlinear activation approach. Given that the large-scale fan-out of optical neural networks (ONNs) will significantly reduce the optical power in one channel, our low-threshold scheme is suitable for the development of high-throughput ONNs.

Photonic parallel channel estimation of MIMO-OFDM wireless communication systems.

Channel estimation is a key technology in MIMO-OFDM wireless communication systems. Increasingly extensive application scenarios and exponentially growing data volumes of MIMO-OFDM systems have imposed greater challenges on the speed, latency, and parallelism of channel estimation based on electronic processors. Here, we propose a photonic parallel channel estimation (PPCE) architecture which features radio-frequency direct processing. Proof-of-concept experiment is carried out to demonstrate the general feasibility of the proposed architecture at different frequency bands (100 MHz, 4 GHz, and 10 GHz). The mean square errors (MSEs) between the experimental channel estimation results and the theoretically simulated ones lie on the order of 10-3. The bit error rates (BERs) are below the pre-forward error correction (pre-FEC) threshold. Besides, we analyze the performance of PPCE under different signal-to-noise ratios (SNRs), baseband symbol forms, and weight tuning precisions. The proposed PPCE architecture has the potential to achieve high-speed, highly parallel channel estimation in large-scale MIMO-OFDM systems after the photonic-electronic chip integration.

Microcomb-based integrated photonic processing unit.

The emergence of parallel convolution-operation technology has substantially powered the complexity and functionality of optical neural networks (ONN) by harnessing the dimension of optical wavelength. However, this advanced architecture faces remarkable challenges in high-level integration and on-chip operation. In this work, convolution based on time-wavelength plane stretching approach is implemented on a microcomb-driven chip-based photonic processing unit (PPU). To support the operation of this processing unit, we develop a dedicated control and operation protocol, leading to a record high weight precision of 9 bits. Moreover, the compact architecture and high data loading speed enable a preeminent photonic-core compute density of over 1 trillion of operations per second per square millimeter (TOPS mm-2). Two proof-of-concept experiments are demonstrated, including image edge detection and handwritten digit recognition, showing comparable processing capability compared to that of a digital computer. Due to the advanced performance and the great scalability, this parallel photonic processing unit can potentially revolutionize sophisticated artificial intelligence tasks including autonomous driving, video action recognition and image reconstruction.

Enhancement of calculation accuracy of the integrated photonic tensor flow processer by global optical power allocation.

We present a global optical power allocation architecture, which can enhance the calculation accuracy of the integrated photonic tensor flow processor (PTFP). By adjusting the optical power splitting ratio according to the weight value and loss of each calculating unit, this architecture can efficiently use optical power so that the signal-to-noise ratio of the PTFP is enhanced. In the case of considering the on-chip optical delay line and spectral loss, the calculation accuracy measured in the experiment is enhanced by more than 1 bit compared with the fixed optical power allocation architecture.

High-order tensor flow processing using integrated photonic circuits.

Tensor analytics lays the mathematical basis for the prosperous promotion of multiway signal processing. To increase computing throughput, mainstream processors transform tensor convolutions into matrix multiplications to enhance the parallelism of computing. However, such order-reducing transformation produces data duplicates and consumes additional memory. Here, we propose an integrated photonic tensor flow processor (PTFP) without digitally duplicating the input data. It outputs the convolved tensor as the input tensor 'flows' through the processor. The hybrid manipulation of optical wavelengths, space dimensions, and time delay steps, enables the direct representation and processing of high-order tensors in the optical domain. In the proof-of-concept experiment, an integrated processor manipulating wavelengths and delay steps is implemented for demonstrating the key functionalities of PTFP. The multi-channel images and videos are processed at the modulation rate of 20 Gbaud. A convolutional neural network for video action recognition is demonstrated on the processor, which achieves an accuracy of 97.9%.

Parallel optical coherent dot-product architecture for large-scale matrix multiplication with compatibility for diverse phase shifters.

Photonics physically promises high-speed and low-consumption computing of matrix multiplication. Nevertheless, conventional approaches are challenging to achieve large throughput, high precision, low power consumption, and high density simultaneously in a single architecture, because the integration scale of conventional approaches is strongly limited by the insertion loss of cascaded optical phase shifters. Here, we present a parallel optical coherent dot-product (P-OCD) architecture, which deploys phase shifters in a fully parallel way. The insertion loss of phase shifters does not accumulate at large integration scale. The architecture decouples the integration scale and phase shifter insertion loss, making it possible to achieve superior throughput, precision, energy-efficiency, and compactness simultaneously in a single architecture. As the architecture is compatible with diverse integration technologies, high-performance computing can be realized with various off-the-shelf photonic phase shifters. Simulations show that compared with conventional architectures, the parallel architecture can achieve near 100× higher throughput and near 10× higher energy efficiency especially with lossy phase shifters. The parallel architecture is expected to perform its unique advantage in computing-intense applications including AI, communications, and autonomous driving.

Photonic ADC-based scheme for joint wireless communication and radar by adopting a broadband OFDM shared signal.

We propose and demonstrate a novel, to the best of our knowledge, joint wireless communication and radar system based on a photonic analog-to-digital converter (PADC), which can receive broadband radio-frequency (RF) signals. Owing to this property, a broadband orthogonal frequency division multiplexing (OFDM) shared signal, which owns obvious advantages in communication applications, can be adopted to realize efficient data communication and high-performance target detection simultaneously. In the experiment, a communication rate of 6 Gbit/s is achieved. Inverse synthetic aperture radar (ISAR) imaging is demonstrated with a two-dimensional (2D) resolution of 3.97 cm × 2.94 cm. Finally, it is verified that high-accuracy radial resolution and high-speed communication can be maintained while increasing the pulse repetition period to detect remote target at around 374.6 m.

Automatic target recognition based on receiver optimization of photonic time-stretched coherent radar.

We demonstrate an automatic target recognition (ATR) scheme based on an improved photonic time-stretched coherent radar (PTS-CR). The reception apertures of the PTS-CR can cover the entire detection range by receiving the echo signal with high repetition rate pulses and increasing the amount of dispersion of the first dispersive medium in the receiver. Two channels with different stretching factors are simultaneously used to restore the signal delay information. Simulated and experimental results verify the feasibility of the new scheme. Finally, based on the improved receiving scheme, PTS-CR successfully performed ATR on four different targets placed on a rotating stage. Combining this with the training of the convolutional neural network (CNN), the recognition accuracy rate is 94.375%.

Multi-band low-noise microwave-signal-receiving system with a photonic frequency down-conversion and transfer-learning network.

In this Letter, we propose and demonstrate a multi-band signal-receiving system, powered by photonic frequency down-conversion and transfer learning. A photonic frequency down-conversion system directly receives the microwave signals, and the transfer-learning network (TLN) lowers the noise in the signals. In addition to the effectiveness of denoising, the TLN also features ultra-fast retraining for signals of different types or different multi-band frequencies. Experimental results showed that the proposed microwave-signal-receiving system can improve the signal-to-noise (SNR) ratio of signals of different types, SNR, and duty cycles. For network retraining, the TLN requires only three times less data and 10 times less time consumption than conventional training methods.

Optical coherent dot-product chip for sophisticated deep learning regression.

Optical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chips. It is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.

Visualizing and simplifying convolutional recurrent autoencoder for mismatch compensation of channel-interleaved photonic analog-to-digital converter.

Deep learning (DL) has been used to successfully solve numerous problems and challenges in a wide range of fields. The architecture of DL is complex and treated as a black box, making it difficult to understand the principles behind it. Here, we visualize the process of compensating for time mismatches for a two-channel photonic analog-to-digital converter (PADC) by a convolutional recurrent autoencoder (CRAE) with excellent generalizability and robustness. Besides, we explore the effects of different modules of the CRAE on the generalizability. Based on the analysis of the above two operations, we simplify the CRAE and then apply it to a four-channel PADC, which is a more complex channel-interleaved system. Consequently, for both PADC systems, the performance of the simplified CRAE is as good as that of the original CRAE. Moreover, for the two-channel PADC, after simplification, the frame rate of the CRAE is increased from 460 frames/second to 975 frames/second, 20,000 points in each frame. For the four-channel PADC, the spur-free dynamic range is enhanced to 24.6 dBc from 5.2 dBc.

Microfluidic Devices for Monitoring the Root Morphology of Arabidopsis Thaliana in situ.

Plant roots play critical roles in absorbing nutrients for the growth and development of plants as well as adapting different environments. Currently, there is no satisfactory way to track dynamic information when studying roots at the high temporal and spatial resolution. Herein, a simple microfluidic device with crossed microchannels was utilized for a microscopic investigation of Arabidopsis thaliana roots in situ. Our experimental results showed that the microfluidic system combined with a microscope could be conveniently utilized for the quantification of primary roots and root hairs with a change of micrometers within a time of minutes. Using the same approach, the influences of high salinity stress could also be investigated on different parts of roots, including the root cap, meristematic zone, elongation zone, mature zone, and root hairs. More importantly, the growth of roots and root hairs could be quantified and compared in a solution of abscisic acid and indole-3-acetic acid, respectively. Our study suggested that the microfluidic system could become a powerful tool for the quantitative investigation of Arabidopsis thaliana roots.

High-accuracy automatic target recognition scheme based on a photonic analog-to-digital converter and a convolutional neural network.

We propose a high-accuracy automatic target recognition (ATR) scheme based on a photonic analog-to-digital converter (PADC) and a convolutional neural network (CNN). The adoption of the PADC enables wideband signal processing up to several gigahertz, and thus high-resolution range profiles (RPs) are attained. The CNN guarantees high recognition accuracy based on such RPs. With four centimeter-sized objects as targets, the performance of the proposed ATR scheme based on the PADC and CNN is experimentally tested in different range resolution cases. The recognition result reveals that high-range resolution leads to high accuracy of ATR. It is proved that when dealing with centimeter-sized targets, the ATR scheme can acquire a much better recognition accuracy than other RP ATR solutions based on electronic schemes. Analysis results also show the reason why higher recognition accuracy is attained with higher-resolution RPs.

Photonic analog-to-digital converter powered by a generalized and robust convolutional recurrent autoencoder.

We propose a convolutional recurrent autoencoder (CRAE) to compensate for time mismatches in a photonic analog-to-digital converter (PADC). In contrast of other neural networks, the proposed CRAE is generalized to untrained mismatches and untrained category of signals while remaining robust to system states. We train the CRAE using mismatched linear frequency modulated (LFM) signals with mismatches of 35 ps and 57 ps under one system state. It can effectively compensate for mismatches of both LFM and Costas frequency modulated signals with mismatches ranging from 35 ps to 137 ps under another system state. When the spur-free dynamic range (SFDR) of the unpowered PADC decreases from 10.2 dBc to -3.0 dBc, the SFDR of the CRAE-powered PADC is over 31.6 dBc.

Modified deep-learning-powered photonic analog-to-digital converter for wideband complicated signal receiving.

We propose and demonstrate a modified deep-learning-powered photonic analog-to-digital converter (DL-PADC) in which a neural network is used to eliminate the signal distortions of the photonic system. This work broadens the receiving capability from simple waveforms to complicated waveforms via implementing a modified deep learning algorithm. Thus, the modified DL-PADC can be applied in real scenarios with wideband complicated signals. Testing results show that the trained neural network eliminates the signal distortions with high quality, improving the spur-free dynamic range by ∼20dB. An experiment for echo detection is conducted as an example, which shows that the neural network enhances the quality of detailed target profile detection. Furthermore, the modified DL-PADC only comprises a low-complexity photonic system, which obviates the requirement for redundant hardware setup while maintaining the processing quality. It is expected that the modified DL-PADC can perform as a promising photonic wideband signal receiver with low hardware complexity.

High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays: erratum.

An erratum to correct a typo in the author list in [Opt. Express27(14), 19778 (2019)].

Optical patching scheme for optical convolutional neural networks based on wavelength-division multiplexing and optical delay lines.

Recent progress on optical neural networks (ONNs) heralds a new future for efficient deep learning accelerators, and novel, to the best of our knowledge, architectures of optical convolutional neural networks (CNNs) provide potential solutions to the widely adopted convolutional models. So far in optical CNNs, the data patching (a necessary process in the convolutional layer) is mostly executed with electronics, resulting in a demand for large input modulator arrays. Here we experimentally demonstrate an optical patching scheme to release the burden of electronic data processing and to cut down the scale of the input modulator array for optical CNNs. Optical delay lines replace electronics to execute data processing, which can reduce the scale of the input modulator array. The adoption of wavelength-division multiplexing enables a single group of optical delay lines to simultaneously process multiple input data, reducing the system complexity. The optical patching scheme provides a new solution to the problem of data input, which is challenging and concerned with the field of ONNs.

Optimization of the Brillouin instantaneous frequency measurement using convolutional neural networks.

The Brillouin instantaneous frequency measurement (B-IFM) is used to measure instantaneous frequencies of an arbitrary signal with high frequency and broad bandwidth. However, the instantaneous frequencies measured using the B-IFM system always suffer from errors, due to system defects. To address this, we adopt a convolutional neural network (CNN) that establishes a function mapping between the measured and nominal instantaneous frequencies to obtain a more accurate instantaneous frequency, thus improving the frequency resolution, system sensitivity, and dynamic range of the B-IFM. Using the proposed CNN-optimized B-IFM system, the average maximum and root mean square errors between the optimized and nominal instantaneous frequencies are less than 26.3 and 15.5 MHz, which is reduced from up to 105.8 and 57.0 MHz. The system sensitivity is increased from 12.1 to 7.8 dBm for the 100 MHz frequency error, and the dynamic range is larger.