Optical-pulse-based reconfigurable phase control for independent beamforming and band fusion in a dual-band phased array receiver.

This Letter proposes an optical-pulse-based reconfigurable phase control method, enabling a dual-band phased array receiver to operate in two modes: dual-band-independent operation and dual-band fusion. The method utilizes optical pulses and optical delay to compensate for phase differences across frequency bands. An electrical phase shifter is employed to compensate for phase residual in both bands. All phase operations to both bands are processed concurrently in one link, thereby maintaining inter-band phase coherence. Experimental results verify the ability of dual-band-independent beamforming and inter-band phase coherence maintaining. A four-channel dual-band (X- and Ku-band) phased array antenna (PAA) receiver is constructed to measure PAA patterns and demonstrate band fusion. The pulse compression results in all directions reveal a doubled improvement in range resolution, which shows the potential for enhancement of radar performance.

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.

Decoupling and one-time elimination of the timing skew in a time-demultiplexing photonic analog-to-digital converter for high-resolution wideband signal acquisition.

Temporal alignment between the demultiplexing signal and sampled signal for complex wideband signals greatly increases the difficulty of designing high-speed and high-resolution photonic analog-to-digital converters (PADCs). We present a vector description to decouple the timing skew from the phase frequency response in time-demultiplexing PADC. We demonstrate that the calibration can be optically implemented with true time delay effects and the broadband input can be one-time calibrated through several single-frequency signals. For verification, we configure out a 40 GSa/s PADC with eight-interleaved sub-channels. The timing skew-induced spurs across the whole Nyquist band are effectively suppressed, making the PADC acquire a wideband signal with 16 GHz instantaneous bandwidth. The spurious-free dynamic range (SFDR) is enhanced to ∼55 dB, and the effective number of bits (ENOB) is improved from ∼5.5 bits to ∼8 bits within 20 GHz. It is nearly 1 bit better than the recently reported time-demultiplexing PADC under the comparable input frequencies.

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.

Loss compensation of an ultra-wideband electro-optic modulator in heterogeneous silicon/erbium-doped lithium niobate.

Electro-optic modulators (EOMs) are indispensable elements for integrated photonic circuits. However, optical insertion losses limit the utilization of EOMs for scalable integration. Here, we propose a novel, to the best of our knowledge, EOM scheme on a heterogeneous platform of silicon- and erbium-doped lithium niobate (Si/Er:LN). In this design, electro-optic modulation and optical amplification are simultaneously employed in phase shifters of the EOM. The excellent electro-optic property of lithium niobate is maintained to achieve ultra-wideband modulation. Meanwhile, optical amplification is performed by adopting the stimulated transitions of erbium ions in the Er:LN, leading to effective optical loss compensation. Theoretical analysis shows that a bandwidth exceeding 170 GHz with a half-wave voltage of 3 V is successfully realized. Moreover, efficient propagation compensation of ∼4 dB is predicted at a wavelength of 1531 nm.

Real-time signal acquisition based on optical excitable response by pre-designing single-tone preamble waveform.

Real-time acquisition of target signals is preferred for mobile communication systems. However, under the requirement of ultra-low latency for next-generation communication, traditional acquisition methods need to temporally locate the target signal from a large amount of raw data with correlation-based computing, introducing extra latency. We propose a real-time signal acquisition method based on an optical excitable response (OER) by pre-designing a single-tone preamble waveform. The preamble waveform is designed to be within the amplitude and bandwidth of the target signal, so no extra transceiver is required. The OER generates a corresponding pulse to the preamble waveform in the analog domain, which simultaneously triggers an analog-to-digital converter (ADC) to acquire target signals. The dependence of OER pulse on the preamble waveform parameter is studied, leading to a pre-design of the preamble waveform for an optimal OER. In the experiment, we demonstrate a millimeter-wave (26.5-GHz) transceiver system with target signals of orthogonal frequency division multiplexing (OFDM) format. Experimental results show that the response time is less than 4 ns, which is far lower than the ms-level response time of traditional all-digital time-synchronous acquisition methods.

Stochastic photonic spiking neuron for Bayesian inference with unsupervised learning.

Stochasticity is an inherent feature of biological neural activities. We propose a noise-injection scheme to implement a GHz-rate stochastic photonic spiking neuron (S-PSN). The firing-probability encoding is experimentally demonstrated and exploited for Bayesian inference with unsupervised learning. In a breast diagnosis task, the stochastic photonic spiking neural network (S-PSNN) can not only achieve a classification accuracy of 96.6%, but can also evaluate the diagnosis uncertainty with prediction entropies. As a result, the misdiagnosis rate is reduced by 80% compared to that of a conventional deterministic photonic spiking neural network (D-PSNN) for the same task. The GHz-rate S-PSN endows the neuromorphic photonics with high-speed Bayesian inference for reliable information processing in error-critical scenarios.

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.

High-speed parallel processing with photonic feedforward reservoir computing.

High-speed photonic reservoir computing (RC) has garnered significant interest in neuromorphic computing. However, existing reservoir layer (RL) architectures mostly rely on time-delayed feedback loops and use analog-to-digital converters for offline digital processing in the implementation of the readout layer, posing inherent limitations on their speed and capabilities. In this paper, we propose a non-feedback method that utilizes the pulse broadening effect induced by optical dispersion to implement a RL. By combining the multiplication of the modulator with the summation of the pulse temporal integration of the distributed feedback-laser diode, we successfully achieve the linear regression operation of the optoelectronic analog readout layer. Our proposed fully-analog feed-forward photonic RC (FF-PhRC) system is experimentally demonstrated to be effective in chaotic signal prediction, spoken digit recognition, and MNIST classification. Additionally, using wavelength-division multiplexing, our system manages to complete parallel tasks and improve processing capability up to 10 GHz per wavelength. The present work highlights the potential of FF-PhRC as a high-performance, high-speed computing tool for real-time neuromorphic computing.

Analog-to-spike encoding and time-efficient RF signal processing with photonic neurons.

The radio-frequency (RF) signal processing in real time is indispensable for advanced information systems, such as radar and communications. However, the latency performance of conventional processing paradigm is worsened by high-speed analog-to-digital conversion (ADC) generating massive data, and computation-intensive digital processing. Here, we propose to encode and process RF signals harnessing photonic spiking response in fully-analog domain. The dependence of photonic analog-to-spike encoding on threshold level and time constant is theoretically and experimentally investigated. For two classes of waveforms from real RF devices, the photonic spiking neuron exhibits distinct distributions of encoded spike numbers. In a waveform classification task, the photonic-spiking-based scheme achieves an accuracy of 92%, comparable to the K-nearest neighbor (KNN) digital algorithm for 94%, and the processing latency is reduced approximately from 0.7 s (code running time on a CPU platform) to 80 ns (light transmission delay) by more than one million times. It is anticipated that the asynchronous-encoding, and binary-output nature of photonic spiking response could pave the way to real-time RF signal processing.

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.

High-resolution ISAR imaging based on photonic receiving for high-accuracy automatic target recognition.

A scheme of high-resolution inverse synthetic aperture radar (ISAR) imaging based on photonic receiving is demonstrated. In the scheme, the linear frequency modulated (LFM) pulse echoes with 8 GHz bandwidth at the center frequency of 36 GHz are directly sampled with the photonic analog-to-digital converter (PADC). The ISAR images of complex targets can be constructed without detection range swath limitation due to the fidelity of the sampled results. The images of two pyramids demonstrate that the two-dimension (2D) resolution is 3.3 cm × 1.9 cm. Furthermore, the automatic target recognition (ATR) is employed based on the high-resolution experimental dataset under the assistance of deep learning. Despite of the small training dataset containing only 50 samples for each model, the ATR accuracy of three complex targets is still validated to be 95% on a test dataset with the equal number of samples.

Equivalence of photonic sampling to signal holding in channel-interleaved photonic ADCs by controlling photo-detection response.

Substantial interests have been attracted in the use of photonic sampling and electronic digitizing for photonic analog-to-digital converter (PADC). However, the nature of that photo-detection with signal holding effects has not been well established. This paper analyzes the equivalence of photonic sampling to signal holding by controlling photo-detection response. In the frequency domain, the high-frequency components generated by the sampling pulse train are folded back into the Nyquist band resulting the signal holding response when the output is digitized. We proposed an approximate response of the photodetector (PD) to verify the theoretical analysis. It is found that the photonic sampling serves as the conventional switch-based sample-and-hold (S&H) circuit in channel-interleaved photonic analog-to-digital converter. In the experiment, the signal holding directly inhibits the timing mismatch without sophisticated calibrations.

Engineering a sandwiched Si/I/LNOI structure for 180-GHz-bandwidth electro-optic modulator with fabrication tolerances.

Electro-optical modulators are essential for scalable photonic integrated circuits and are promising for many applications. The convergence of silicon (Si) and lithium niobate (LN) allows for a compact device footprint and large-scale integration of modulators. We propose a sandwiched Si/I/LNOI modulator for broad modulation with CMOS-compatible fabrication tolerances. There is a thin film SiO2 spacer sandwiched between Si and LN, which is engineered to tailor optical and electrical properties and enhance index matching. Moreover, the SiO2 spacer is also exploited to inhibit the radiation loss induced by mode coupling. The modulator shows a bandwidth of ∼180 GHz with a halfwave voltage of 3 V. Such a device is considerably robust to the fabrication deviations, making it promising for massive and stable manufacturing.

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.

Ultrabroad Brillouin instantaneous frequency measurement with a designed linear system response.

We demonstrate an ultrabroad instantaneous frequency measurement (IFM) based on stimulated Brillouin scattering (SBS) with a designed linear system response. The linear system response is found to be the key factor that broadens the system bandwidth. It is realized by designing the sweeping method of frequency and amplitude of the local pump signal. With the improvement of linearity, the measurement error is decreased and the bandwidth of the SBS-based IFM is consequently enlarged. A Costas frequency modulated signal with an instantaneous bandwidth of 10.5 GHz is successfully measured by the designed system response. Further optimization of pump signal's characteristics extends the system bandwidth to 14.5 GHz. The measurement error of a linear frequency modulated (LFM) signal ranging from 6 GHz to 20.5 GHz is less than 1% of the instantaneous bandwidth.

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%.