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Photonic reservoir computing (RC) is a simple and efficient neuromorphic computing framework for human cortical circuits, which is featured with fast training speed and low training cost. Photonic time delay RC, as a simple hardware implementation method of RC, has attracted widespread attention. In this paper, we present and experimentally demonstrate a time delay RC system based on a Fabry Perot (FP) laser for multiple tasks processing. Here, the various tasks are attempted to perform in parallel in the multiple longitudinal modes of the FP laser. It is found that the time delay RC system based on the FP laser can successfully handle different tasks across multiple longitudinal modes simultaneously. The experimental results demonstrate the potential of the time delay RC system based on the FP laser to achieve multiple tasks processing, providing various possibilities for improving the information processing ability of neural morphology RC systems, and promoting the development of RC systems.

We report on a novel structure of a receptive field (RF)-based multi-layer all-optical neural network using a micropillar laser with a saturable absorber (SA) for image processing. From the perspective of biological vision, the realization of image processing based on the RF provides the biological rationality for the machine vision implemented by the spiking neural network (SNN). By exploiting the fast physical mechanisms of gain and absorption in the SA laser, the photonic spike-timing-dependent plasticity (STDP) curves are achieved to train the weights. Here, the source image pixels are mapped into the temporal information of spike trains injected into the neural network through the temporal coding method called time-to-first-spike encoding. Different source images are processed and tested by the proposed photonic SNN. Simulation results show that our proposed system can process not only simple binary images but also complex color images under the adjustment of STDP rules. When considering the robustness, we demonstrate the tolerance of the image segmentation to the time jitter. These results indicate that our proposed photonic SNN can achieve high-resolution processing of complex source images. Additionally, the time-multiplexing technique can be further adopted to simplify the RF structure, which is expected to reduce the complexity of the whole system, thus facilitating physical applications. Our work offers the prospect for a high-speed photonic spiking platform for image processing.

Optical acceleration of neuromorphic computing has emerged as a platform for achieving low-latency and energy-efficient computation. Due to the rich dynamics, the integrated photonics architecture provides a promising way for implementations of nonlinear computing. In this article, we demonstrate pattern classification of 2 benchmark datasets, the Iris dataset and the Wisconsin Breast Cancer (WBC) dataset, based on the nonlinear neuron-like dynamics of an integrated Fabry-Perot laser chip with a saturable absorption region (FP-SA). The effectiveness and robustness of the algorithm are firstly verified by numerical simulations. With the help of an optimized delay learning method, efficient learning can be achieved based on a single neuron, achieving 96% and 92% in classification accuracy of Iris and WBC dataset respectively. Then, the hardware-algorithm collaborative computing is demonstrated based on a single FP-SA laser chip. The classification accuracy of Iris and WBC dataset could reach 94.67% and 88%, respectively, having a relative low loss compared to the simulation results. This work provides an efficient solution for classification tasks based on optical SNN.

Spiking neural networks (SNNs) communicate via discrete spikes, necessitating the conversion between spike signals and real-valued signals for optimal encoding efficiency and performance. Here, we design and fabricate a photonic spiking neuron chip based on a distributed feedback laser with a saturable absorber (DFB-SA), and propose two novel spike-based temporal encoding schemes for Exclusive OR (XOR) operation by employing the refractory period and temporal integration characteristics. More specifically, different inputs are temporally-encoded as a single spike at different timings. The outputs are encoded as a single spike (‘1’) or double spikes (‘0’) for the type I encoding scheme based on the refractory period property, but are encoded as a single spike (‘0’) or no spike (‘1’) response for the type II encoding scheme based on the temporal integration effect. The effects of bias current, injection power, and reverse bias voltage on the refractory period and temporal integration dynamics of the fabricated DFB-SA laser are experimentally investigated. We further experimentally demonstrate reproducible and stable XOR operation based on the two proposed spike-based encoding approaches. Moreover, numerical results based on the Yamada model reproduce well the experimental findings. Our spike-based temporal encoding approaches show promise in improving the encoding efficiency of neuromorphic SNNs.

Neuromorphic computation based on physical devices has attracted wide attention due to its high performance in solving specific tasks. The chosen devices usually imitate the way the brain transmits information and calculates. However, the majority of existing encoding methods for neuromorphic computation rely on either rate or temporal information, resulting in inefficiency in accomplishing computing tasks. Here, we present an efficient and biologically plausible encoding method for a photonic spiking neural network (PSNN) based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSELs-SA). In the proposed method, the date information is encoded into the strength of the injection rectangular pulse and then converted into spikes emitted by VCSELs-SA. We can obtain timing and threshold characteristics by adjusting the injection intensity which is beneficial to process the data in the following steps. The encoding method is verified by recognizing four spiking patterns and four expressions. Furthermore, we classify the Iris data set based on the encoding method with only four pre-neurons and two post-neurons. The encoding method and structure further explore the application of PSNN to pave the way for the hardware implementation.

Spiking neural network (SNN) have attracted lots of attention due to its event-driven nature and powerful computation capability. However, it is still limited to simple task due to the training difficulty. In this work, we propose a hybrid architecture of photonic convolutional spiking neural network (PCSNN) to realize the speech recognition task. In the PCSNN, the feature extraction is realized by a convolution SNN with unsupervised learning algorithm, the classification is realized by a photonic SNN with modified time-based supervised training algorithm. The TIDIGITS dataset is used to test the speech recognition performance of the proposed PCSNN, and the highest testing accuracy is 93.75%. The proposed PCSNN provides a solution for architecture and algorithm co-design for the speech recognition task, which is helpful for extending the applications of photonic SNN.

Photonic technologies offer great prospects for novel ultrafast, energy-efficient and hardware friendly neuromorphic computing platforms. Moreover, Photonic spiking neural network (PSNN) emerged as a promising approach toward building a low-latency and energy-efficient non-von-Neuman computing system is of particular interest. In this article, a fabricated Fabry–Pérot laser with saturable absorber (FP-SA) is employed as the photonic spiking neuron of the PSNN, and a multi-layer PSNN with such three cascaded photonic spiking neurons is proposed. The cascadability of the proposed multi-layer PSNN is demonstrated experimentally. It is found that the performance of the pattern recognition task using the hardware-algorithm collaborative computing is improved in the multi-layer PSNN. Besides, the nonlinear neuron-like dynamics including temporal integration and threshold in such multi-layer PSNN are also experimentally investigated, and better nonlinear neuron-like dynamics can be achieved. Such proposed multi-layer PSNN potentially opens up the prospect of employing simple hardware structure and cooperating with algorithm design to realize a photonic depth neural network to solve more complex problems.

Adversarial attacks have been extensively investigated for machine learning systems including deep learning in the electronic domain. However, the adversarial attacks on optical neural networks (ONNs) have been seldom considered previously. In this work, we first construct an accurate image classifier with an ONN using a mesh of interconnected Mach–Zehnder interferometers (MZIs). Then a corresponding adversarial attack scheme is proposed for the first time. The attacked images are visually very similar to the original ones but the ONN system becomes malfunctioned and generates wrong classification results in most time. The results indicate that adversarial attack is also a significant issue for optical machine learning systems.

We propose and demonstrate a plastic photonic synapse to achieve all-optical synaptic plasticity based on the potentiated and depressed dynamics in commercially available vertical-cavity semiconductor optical amplifier (VCSOA). The effects of presynaptic and postsynaptic spike powers, bias current, initial wavelength detuning between presynaptic spike and VCSOA on the potentiated and depressed time windows are investigated. The numerical results show that all-optical synaptic plasticity can be achieved successfully based on VCSOA with delayed self-feedback loop. The proof-of-concept experimental results show that plastic photonic synapse can be achieved based on the VCSOA with subsequent iterations. Besides, the curve of VCSOA gain in the potentiated and depressed time windows is similar to that of spike timing dependent plasticity. Furthermore, all-optical unsupervised self-learning and recognition of first spike timing of an input pattern are realized numerically in the photonic neural network including two proposed plastic photonic synapses. The plastic photonic synapse based on a VCSOA is interesting and valuable for the self-learning in all-optical neural networks. Moreover, the proposed plastic photonic synapse offers great potential for dense, high-performance neuromorphic photonic systems.

We propose an inferencing framework of a hybrid-integrated photonic spiking neural network (PSNN) to perform pattern recognition tasks, where the linear computation is realized based on a 4 × 4 silicon photonic Mach-Zehnder interferometer (MZI) array, and the nonlinear computation is performed by an InP-based spiking neuron array based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSELs-SA). With the modified Tempotron-like remote supervised method (ReSuMe) training algorithm, we realize two pattern recognition tasks, the recognition of numbers “0-3” and optical character recognition (OCR). The phase shifts in the MZI array are accurately configured to represent the weight matrix according to the decomposition procedure of a 4 × 4 triangular MZI mesh. Besides, the effects of the phase shift error and quantization precision of phase shifters (PSs) on the recognition performance are analyzed. For the OCR task, the 400 × 10 PSNN is realized by multiplexing the 4 × 4 MZI array based on the matrix blocking and the reconfigurability of the MZI array. This work provides a systematic computational model of the hybrid-integrated PSNN based on the silicon photonics and InP platforms, enabling the co-design and optimization of hardware architecture and algorithm, which contributes one step forward toward the construction of a hybrid-integrated PSNN hardware system.

In conclusion, a scheme for broadband chaos generation is proposed and demonstrated based on a VCSEL with subharmonic modulation optical injection. A 1/2 sub-harmonic modulation signal is injected into the VCSEL to destabilize the P1 state and generate a complex chaos signal with a bandwidth above 20 GHz. Moreover, such a chaos signal has been used as a physical entropy source to produce the physical number. By adopting a 4-LSBs extraction method, we can realize a 320 Gb/s physical number stream with verified randomness. Our scheme may offer a new alternative solution for ultrafast physical number generation using a single VCSEL.

Objective: To observe the effects of electroacupuncture (EA) pretreatment on M1 polarization of alveolar macrophages (AMs) in rats with acute lung injury (ALI) induced by lipopolysaccharide (LPS), and to explore the potential protective mechanism of EA. Methods: Forty Sprague-Dawley rats were randomly divided into a normal group, a model group, and three groups of EA pretreatment [including a Chize (LU5) group, a Zusanli (ST36) group and a Chize (LU5) plus Zusanli (ST36) group], with eight rats in each group. The model rats of ALI were established by instilling LPS [2 mg/(kg-bw)] into the trachea of rats for 3 h. The rats in each EA pretreatment group were pretreated with EA for 30 min per day at the corresponding bilateral acupoints 6 d before instilling LPS. Three hours after modeling, the pulmonary function of the rats was tested, and the lung tissue was taken to calculate the ratio of lung wet weight to dry weight (W/D). The pathological lung changes and the injury score were observed by hematoxylin-eosin staining. The contents of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and myeloperoxidase (MPO) in rat’s bronchoalveolar lavage fluid (BALF) were detected by enzyme-linked immunosorbent assay. The mRNA and protein expression levels of Ml macrophage markers clusters of differentiation 86 (CD86), inducible nitric oxide synthase (iNOS), and its signaling pathway factor Toll-like receptor (TLR) 4, and nuclear factor-KB (NF-kB) p65 in the alveoli were detected by fluorescence quantitative polymerase chain reaction and Western blot, respectively. Results: After being induced by LPS, the pulmonary function of the model rats showed that the forced expiratory volume in 0.1 s (FEV0.1), forced expiratory volume in 0.3 s (FEV0.3), and their respective ratios of FEV to forced vital capacity (FVC) (including FEV0.1/FVC and FEV0.3/FVC) were significantly decreased (P<0.01), while the W/D of lung tissue was increased (P<0.01). The score of lung injury was significantly higher (P<0.01). The contents of TNF-α, IL-1β, and MPO in the BALF and the mRNA and protein expression levels of CD86, iNOS, TLR4, and NF-kB p65 in the lung tissue were significantly increased (P<0.01). After EA pretreatment, the FEV0.1, FEV0.3, FEV0.1/FVC, and FEV0.3/FVC were significantly increased, the lung injury score decreased significantly, and the contents of TNF-α, IL-1β, and MPO in the BALF and the expression levels of CD86, iNOS, TLR4, and NF-kB p65 mRNAs and proteins in the alveoli decreased significantly (P<0.05 or P<0.01). Compared with the other two single acupoint groups, the contents of TNF-α, IL-1Pβ, and MPO in the BALF and the expression levels of CD86, iNOS, TLR4, and NF-kB p65 mRNAs in the alveoli in the Chize (LU5) plus Zusanli (ST36) group were significantly lower (P<0.01). Conclusion: EA pretreatment at Chize (LU5) and Zusanli (ST36) can inhibit inflammation and reduce pulmonary injury in ALI rats induced by LPS. The effect of the combination of Chize (LU5) and Zusanli (ST36) is better than that of using these two acupoints separately, and its mechanism may be related to the inhibition of AMs’ M1 polarization by down-regulation TLR4/NF-KB signaling pathway.

We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback (DFB) laser subject to side-mode optical pulse injection (SMOPI). The DFB laser chip is designed and fabricated based on asymmetric equivalent πphase shift (π-EPS) with the reconstruction-equivalent-chirp (REC) technique. Under side-mode continuous-wave (CW) optical injection, excitability pulse was experimentally observed during the dominant mode switching process due to the injection-locked effect. Based on the transition between the excitability regime and the side-mode injection locking effect, the controllable and repeatable neuron-like spiking response can be realized when external stimulus pulses are electro-optically modulated on the CW optical carrier. The experimental results show that the spike threshold, temporal integration, and refractory period, which are important spike processing mechanisms in biological neurons, can all be achieved in the optically-injected DFB laser. The experimental findings are also verified numerically with a rate equation model that considers the SMOPI. To the best of our knowledge, this is the first experimental demonstration of a photonic spiking neuron based on a DFB laser sub ject to SMOPI, which holds promise for realizing large-scale photonic spiking neuron arrays for hardware photonic spiking neural network chips.

We propose computing primitive for an all-optical spiking neural network (SNN) based on vertical-cavity surface-emitting lasers (VCSELs) for supervised learning by using biologically plausible mechanisms. The spike-timing-dependent plasticity (STDP) model was established based on the dynamics of the vertical-cavity semiconductor optical amplifier (VCSOA) subject to dual-optical pulse injection. The neuron-synapse self-consistent unified model of the all-optical SNN was developed, which enables reproducing the essential neuron-like dynamics and STDP function. Optical character numbers are trained and tested by the proposed fully VCSEL-based all-optical SNN. Simulation results show that the proposed all-optical SNN is capable of recognizing ten numbers by a supervised learning algorithm, in which the input and output patterns as well as the teacher signals of the all-optical SNN are represented by spatiotemporal fashions. Moreover, the lateral inhibition is not required in our proposed architecture, which is friendly to the hardware implementation. The system-level unified model enables architecture–algorithm codesigns and optimization of all-optical SNN. To the best of our knowledge, the computing primitive of an all-optical SNN based on VCSELs for supervised learning has not yet been reported, which paves the way toward fully VCSEL-based large-scale photonic neuromorphic systems with low power consumption.

Here we propose and experimentally demonstrate mutually coupled 1550 nm vertical-cavity surface-emitting lasers (VCSELs), subject to common fiber Bragg grating (FBG) feedback (FMC-VCSELs), to conceal the time-delay signature (TDS) of chaotic outputs. The autocorrelation function and delayed mutual information are used to quantitatively identify the TDS of chaotic output. For comparison, the evolution of the TDS of chaotic output in mutually coupled VCSELs (MC-VCSELs) is also presented. The effects of injection power, frequency detuning between two VCSELs, and frequency detuning between FBG and VCSELs on the TDS concealment are experimentally measured. Experimental results show that FMC-VCSELs have a better TDS suppression performance than MC-VCSELs for varying injection power. In addition, for the FMC-VCSELs system, the TDS can be suppressed to below 0.1 when the VCSELs x-polarization component is located at the edge of the main FBG lobe. Furthermore, dual-channel physical random numbers with verified randomness at a rate of 800 Gbps (2×5LSBs×80GHz) are achieved by utilizing the chaotic outputs with a low TDS from two VCSELs in the FMC-VCSELs system.

We propose a simple hardware architecture for solving exclusive OR (XOR) tasks in a single step by using a single photonic spiking neuron based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA) subject to dual-polarized pulsed optical injection. We model the inhibitory photonic spiking neuron by extending the Yamada model and spin-flip model to incorporate the two polarization-resolved modes and the saturable absorber. It is shown that, by carefully adjusting the temporal difference according to the inhibitory window, the XOR operation can be realized in a single photonic spiking neuron, which is interesting and valuable for the photonic neuromorphic computing and information processing.

In this paper, we propose and demonstrate experimentally and numerically a hierarchical time-delay optical reservoir computing (RC) system based on cascaded vertical-cavity surface-emitting lasers (VCSELs) with feedback and multiple injections. The prediction performance characteristics of the hierarchical time-delay RC system based on cascaded VCSELs under different reservoir layers are compared. Evidently, the prediction performance of the hierarchical time-delay RC system is first improved and saturates as the number of reservoir layers increases. Besides, the impacts of key factors on predicting the hierarchical time-delay RC system are also analyzed in detail experimentally and numerically. This proposed hierarchical time-delay RC system based on VCSELs is useful for the further development of RC systems and may be beneficial to improve the ability of RC systems to solve more complex problems.

This work presents a complete conversion scheme for photonic spiking neural networks (SNNs). We verified that the output of an artificial neural network (ANN) trained with the simulated optical activation function can be directly converted into the spike rate of a photonic spiking neuron model. To reveal the feasibility of hardware implementation, we considered the effects of different bit precisions of data and weight, noise level, and bias current mismatch on the converted results. The proposed scheme was evaluated using the Deterding vowel, IRIS, TIDIGITS, and MNIST datasets for pattern recognition, and achieved mean accuracies of 95.80%, 98.67%, 96.19%, and 92.33%, respectively. The proposed scheme can convert an ANN into a photonic SNN with almost no precision loss, and the performance was comparable to that of an ANN trained with the rectified linear unit function. The proposed scheme can enable the high-performance implementation of photonic SNNs.

We have investigated the cluster isolated desynchronization, a symmetry-breaking state, in the delay-coupled vertical-cavity surface-emitting lasers (VCSELs) networks subject to variable-polarization optical feedback (VPOF). It is shown that, in the VPOF-VCSELs networks, the elusive isolated desynchronization phenomenon could emerge out of the cluster synchronization by the common-signal injection approach from an additional auxiliary VCSEL. The influences of parameters in VPOF-VCSELs networks on the existence and stability of isolated desynchronization are systematically investigated. Moreover, the generality of the proposed scheme is validated in the VPOF-VCSELs network with real-world network topology (Nepal power grid network). Our results offer a new insight to manage the synchronization patterns of a VCSELs network.

The generation and storage properties of different spike codes in vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSELs-SA) are investigated numerically. The results show that different spike codes are generated by injecting an optical pulse into one single VCSEL-SA and can be stored in two mutually coupled VCSELs-SA. In particular, in the case of the generation of spike codes, the effects of the input strength and the temporal duration of the input optical pulse are studied; in the case of the storage of spike codes, the roles of the coupling weight and the coupling delay between the two mutually coupled VCSELs-SA are examined. Simulations reveal that spikes can be triggered if the input strength and the temporal duration exceed the threshold values, and higher values of the input strength and the temporal duration are beneficial for generating more spikes. Moreover, successful storage of a perfectly formed train of spike codes in two mutually coupled VCSELs-SA can also be realized provided that the coupling weight and the coupling delay are larger than the corresponding threshold values.

The zero-lag correlation properties of intensity chaos in ring networks consisting of three semiconductor lasers mutually coupled with heterogeneous time delays are numerically investigated. The role of heterogeneous coupling delays with specific ratios on the zero-lag chaotic intensity correlation is revealed, and the effects of coupling rate, frequency detuning, and time delay heterogeneity are also considered. Furthermore, by introducing imaginary nodes to map the heterogeneous ring network to equivalent the homogeneous (EHM) ring network with identical delay, the relationship between such zero-lag correlation properties of intensity chaos and the greatest common divisor (GCD) of the EHM ring network loop length is revealed. It is shown that, when the coupling rate is sufficiently large, two types of zero-lag synchrony patterns, zero-lag global synchrony and zero-lag cluster synchrony, can be found in the same ring network, depending on the GCD of the delay loops composing the network. That is to say, by carefully adjusting the coupling delays ratio one can deliberately switch between two different zero-lag synchrony patterns in the same laser ring network, which may be interesting for the field of chaos synchronization. High-quality synchronization can be obtained in wide range of coupling rate, and is robust to small frequency detuning and slight mismatched internal laser parameters. The dependence of zero-lag synchronization of intensity chaos may provide useful guidelines for designing multi-user chaos optical communication among ring networks.

The complexity properties of polarization-resolved chaotic signals generated in a ring network of vertical-cavity surface-emitting lasers (VCSELs) mutually coupled with multiple delays are investigated quantitatively by using the proposed mean permutation entropy (MPE). For direct comparison, the complexity of polarization-resolved chaos in a ring network of VCSELs coupled with single delay is also considered. The effects of injection current, coupling strength, and frequency detuning on the chaotic complexity for both a single-delay ring network (SDRN) and a multiple-delay ring network (MDRN) are evaluated quantitatively and compared by the MPE. The effects of internal parameters of VCSELs on the complexity are also discussed, and the correlation properties between different polarization-resolved modes are also analyzed. It is shown that the complexity of chaos in two polarization-resolved modes of VCSELs in MDRN is much higher than those in SDRN in a much wider parameter region. Besides, wider range of injection current, coupling strength, and frequency detuning can be tuned to achieve the enhancement of chaotic complexity in MDRN. These results provide an effective quantifier, the proposed MPE, to evaluate quantitatively the complexity of chaos generated in systems with multiple delays, and the multichannel complexity-enhanced polarization-resolved chaos generated in MDRN of mutually coupled VCSELs is extremely meaningful for the chaos-based random number generators.

In this paper, a novel photonic-assisted Doppler frequency shift (DFS) measurement scheme based on an integrated dual-polarization Mach–Zehnder modulator is presented. In the proposed scheme, the DFS to be identified is transformed into a low-frequency electrical signal through an optical frequency-conversion link. The value of the DFS can be acquired by analyzing the spectrum of the low-frequency electrical signal. Meanwhile, the orientation of the DFS can be easily determined utilizing a 90° hybrid coupler. If the receiver is moving toward the transmitter, only the positive port has an output signal, while only the negative port has an output signal if the receiver is moving away from the transmitter. The scheme can simultaneously obtain the value and the orientation of the DFS. In addition, to investigate the frequency tunability of the proposed scheme, the DFS, which varies from −100 to 100 KHz at a step of 10 KHz for different microwave signals at frequencies of 10, 15, and 18 GHz, is demonstrated experimentally, and the errors are within ±5×10^−6  Hz.

This paper presents a novel photonic-assisted microwave frequency measurement scheme based on an integrated dual-polarization Mach–Zehnder modulator (DPol-MZM). The DPol-MZM is used to obtain a polarization multiplexing signal modulated by the microwave signal with the frequency to be identified. The obtained signal is split into two channels after propagating along a single mode fiber. The two divided parts are used to establish an amplitude comparison function (ACF) which provides frequency-power mapping. The proposed scheme is experimentally verified. The frequency responses of the two branches are nearly complementary; thus, a relatively steep ACF is obtained. A frequency measurement range from 2 to 28 GHz with an error of ±0.2  GHz is achieved. Moreover, the measurement range can be tuned by simply adjusting the polarization state of one channel. The proposed system is simple, and the measurement range can be easily adjusted.

We experimentally design two photonic spike-timing-dependent plasticity (STDP) schemes based on a single vertical-cavity semiconductor optical amplifier (VCSOA) and demonstrate the photonic implementation of STDP characteristics. In the first scheme, a single-polarized optical pulse train is injected into the VCSOA, in which a pair of optical pulses with a time difference is designed to emulate the pre-synaptic and post-synaptic spikes. In the second scheme, dual-polarized optical pulses emulating the pre-synaptic and post-synaptic spikes are injected into the single VCSOA. Furthermore, the effects of the initial wavelength detuning and the power of the input optical pulse on the STDP curve are analyzed. The proposed photonic STDP schemes based on a single VCSOA need relatively low bias current and power consumption, and thus, are ideal optical synaptic devices forming key components in the construction of the photonic neuromorphic computing system.

A simultaneous unidirectional and bidirectional chaos-based optical communication scheme based on a hybrid coupling semiconductor laser system that consists of one center semiconductor laser (CSL) and multiple side semiconductor lasers (SSLs) is proposed numerically. In this scheme, the SSLs oriented in a linear chain mutually couple with the adjacent SSLs and they are subjected to identical unidirectional injections from the CSL which is a chaotic external cavity semiconductor laser. We theoretically analyze the conditions for different types of chaos synchronization based on the symmetry operation mechanism and injection-locking mechanism, and numerically investigate the influences of operation parameters, parameter mismatch robustness, chaos pass filtering effects and communication performance of the hybrid coupling semiconductor laser system. The simulation results demonstrate that with proper selection of the unidirectional and mutual coupling conditions, the SSLs can synchronize with each other isochronally and simultaneously synchronize to the CSL completely or laggardly, which enables the proposed system to achieve a unidirectional broadcasting communication from the CSL to the SSLs and a bidirectional communication among the SSLs simultaneously. The proposed scheme is beneficial to the implementation of optical chaos communication networks.

We report on the realization of a hybrid chaos-based communication scheme using three chaotic semiconductor ring lasers (SRLs). In this scheme, two slave SRLs (S-SRLs) are identically driven by a master SRL (M-SRL) subject to delayed optical feedback. Under proper conditions, the S-SRLs are completely synchronized with each other due to the symmetric operation, and they are also synchronized with the M-SRL through the injection-locking effect. The results also show that a message encrypted through chaos shift keying at the M-SRL end can be successfully decrypted by the two S-SRLs, while the two uncoupled S-SRLs allow for dual-channel chaos communication when both counterpropagating modes of one S-SRL are encoded with a message.

The gain enhancement properties of a fiber optical parametric amplifier (FOPA) are investigated by inserting a phase-shifted fiber Bragg grating (PS-FBG) between two highly nonlinear dispersion shift fibers (HNL-DSFs). The PS-FBG is adopted to reduce the power and change the phase of the idler wave, and thus to change the phase mismatch parameter and relative phase difference among the four involved waves in four-wave mixing (FWM). The influence of reflectivity and phase shift of the PS-FBG on the gain properties of FOPA is focused on. It is shown that, with the introduction of PS-FBG, the gain of FOPA is enhanced significantly. With the increase of reflectivity of PS-FBG, the gain increases first and reaches its maximum at the optimal reflectivity. Besides, the π-phase shift contributes to the highest gain of FOPA. The effect of insertion loss is also considered. The FOPA with PS-FBG provides a new tool to obtain gain enhancement, which is extremely useful for the optical communication system.

In this paper, a secure optical chaos-based communication scheme is proposed. A semiconductor laser (SL) subjected to polarization-rotated optical feedback serves as the drive laser (DL), and two identical or near-identical SLs act as the transmitter laser (TL) and receiver laser (RL). The TL and the RL receive identical polarization-rotated optical injection from the DL. Two channels are used, and the TM mode of the DL, after being rotated by 90°, serves as the perturbation quantity. The communication performance is numerically studied. More importantly, the high fidelity and high degree of privacy of the proposed scheme are verified in detail, and the failure of two potential attacker scenarios is demonstrated, even if the attacker can get access to both channels simultaneously.

Sample entropy is introduced as a versatile measure for analyzing chaos dynamics and chaos communications. This measure can be directly applied to distinguish different dynamic behaviors for their superior advantages, such as fast computation (available for short data), extreme simplicity, robustness, and even more efficiency in the presence of a certain amount of noise. For the unidirectional coupling master–slave configuration of semiconductor lasers, it is shown that sample entropy analysis can be an effective tool to reveal the chaos synchronization quality. More particularly, when employed to discriminate the encryption performance of a chaos modulation scheme, the sample entropy algorithm seems to be a powerful and complementary tool for detecting and quantifying the presence of a message within a chaotic carrier.

We numerically demonstrate that stable isochronal chaos synchronization (ICS) can be achieved by adding individual self-feedback or external driving to two multiple time-delayed coupling semiconductor lasers (MTDCSLs). The properties of the ICS, such as the influences of the coupling parameters, the mismatch robustness, the chaos pass filtering effects, and the communication performance in these two MTDCSL configurations, are comparatively studied. Moreover, we show that the proposed schemes afford multichannel chaos-based communication with the chaos modulation technique, which provides a potential method for the realization of chaos communication networks.

The preservability of the chaos synchronization in an intermittent unidirectional coupling semiconductor laser configuration is numerically investigated. By adding additional feedback to the receiver laser, we find that the complete synchronization can be preserved for several tens of nanoseconds after the coupling is cut off, and longer feedback and coupling delays can provide better preservability. Moreover, when the conversion frequency of the coupling is properly high, the synchronization can be preserved all the time. As a consequence of these results, a one-to-two chaos synchronization system is achieved, which provides an opportunity for the implementation of one-to-many and many-to-many chaos synchronization.

The hysteresis properties in bistable polarization switching (PS) of vertical-cavity surface-emitting lasers (VCSELs) induced by variable-polarization optical feedback (VPOF) are investigated numerically. We mainly concentrate on the influence of sweep rate of the polarizer angle on the bistable PS for the cases with different feedback strengths and feedback delays. The output in the time domain and the representation on the Poincaré sphere plot are further presented to describe the two PS processes. The results show that the size of the hysteresis cycle of the PS follows a power-law relationship versus the sweep rate of the polarizer angle, and the feedback strength and feedback delay can modify the hysteresis properties significantly. A larger feedback strength leads to a narrower hysteresis loop, while a larger feedback delay gives rise to a wider hysteresis loop. Therefore the VPOF provides a new method for obtaining the controllable bistable PS in VCSELs, which is potentially interesting for applications that require polarization-bistable VCSELs.

The polarization degree of vertical-cavity surface-emitting lasers (VCSELs) subjected to optical feedback with rotating polarization is investigated numerically. To compare the polarization properties of two VCSELs with different frequency splitting (i.e., VCSEL1/VCSEL2 with closely/widely spaced frequency splitting), we extend the spin-flip model by taking the controllable polarization into consideration. The degrees of polarization are quantified in terms of the defined fractional polarization (FP), and the evolutions of the polarization state are further described to explain the polarization switching process through the representation on the Poincaré sphere. As the polarizer angle is varied from 0° to 90°, the polarization switching and the competition between the linearly polarized and the elliptically polarized states are observed in both VCSELs. However, the switching processes are different: in VCSEL1 the polarization varies slowly from x-polarization (XP) mode to y-polarization (YP) mode along the linearly polarized state oriented between the two modes, while the switching is much sharper and the so-called critical angle corresponding to the minimum FP is found in VCSEL2. The effects of the initial conditions, spin-flip rate, and feedback strength are also taken into account.

Influences of variable-angle polarization-rotated optical feedback on polarization properties of vertical-cavity surface-emitting lasers (VCSELs) are investigated numerically. For the so-called case A (x polarization) feedback, only the x mode is selected to pass through the feedback loop. As the polarization angle is varied from 0° to 90°, the dominant polarization mode switching can be observed even for fixed feedback strength and bias current. For the so-called case B (xy polarization) feedback, the total outputs of the VCSEL pass through the feedback loop. The polarization property is much different from case A. The dominant polarization mode switching occurs not in the entire range of polarization angles, however, but in the so-called critical angle, from where the intensities of both polarization modes become comparable to each other and keep around constant values, and can be observed. In addition, the complementary properties of intensities between the two polarization modes for both cases are evaluated quantitatively in terms of the defined normalized intensity.

Integrated circuits (ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called “Moore’s law” in the past 60 years, and now has entered the post Moore’s law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spike. Based on the proposed supervised learning rule, 10 digital images are learned in photonic neural network which consists of 30 presynaptic neurons and 10 postsynaptic neurons. Presynaptic and postsynaptic neurons are photonic neurons based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA). The results show that 10 digital images are recognized correctly in photonic SNN after enough training. Additionally, the effects of learning rate, the jitters of learning rate, initial weights distribution of SNN and bias current of postsynaptic neurons (VCSELs-SA) on the recognized error are examined carefully based on the proposed learning rule. To the best of our knowledge, such modified supervised learning rule has not yet been reported, which would contribute to training photonic neural networks, and hence is interesting for neuromorphic photonic systems and pattern recognition.

The effects of unpredictability degree on the chaos synchronization properties of vertical-cavity surface-emitting lasers with variable-polarization optical feedback are investigated numerically. For variable-polarization optical injection, only low-unpredictability chaos can be well synchronized, while high-unpredictability chaos cannot be synchronized even with large injection strength. On the other hand, for the polarization-preserved optical injection, the synchronization quality is hardly affected by the unpredictability degree, and high-quality synchronization can be achieved for both low- and high-unpredictability chaos due to injection locking.

We numerically analyze the effect of an injection current on the synchronization and communication features of two chaotic external cavity semiconductor lasers in a closed-loop configuration. The simulation results demonstrate that, by moderately increasing the injection current, the efficient bandwidth of the chaotic carrier would be broadened, high quality chaos synchronization can be preserved, and the communication performance can be enhanced.

The chaotic unpredictability of vertical-cavity surface-emitting lasers (VCSELs) with polarization-rotated optical feedback is evaluated quantitatively via the normalized permutation entropy H. The effect of polarization mode competition (PMC) is explored. For weak PMC, the H for X-polarization mode, Y-polarization mode, and total output are close to each other, while for strong PMC, the H for the total output is the highest one, which indicates that the permutation entropy is an effective tool for quantifying chaotic unpredictability of VCSELs and provides valuable information for choosing the proper chaotic carrier.

In this paper, a neuromorphic reservoir computing (RC) system with enhanced memory capacity (MC) based on a vertical-cavity surface-emitting laser (VCSEL) subject to double optical feedbacks (DOF) is proposed and investigated numerically. The aim of this study is to explore the MC of the proposed system. For the purpose of comparison, the MC of the VCSEL-based RC system with single optical feedback (SOF) is also taken into account. It is found that, compared with the VCSEL-based RC system subject to SOF, enhanced MC can be obtained for the VCSEL-based RC system with DOF. Besides, the effects of feedback strength, injected strength, frequency detuning as well as injection current on the MC of the VCSEL-based RC system with DOF are considered. Moreover, the influence of feedback delays is also carefully examined. Thus, such proposed VCSEL-based RC system with DOF provides a prospect for the further development of the neuromorphic photonic system based on RC.

We propose and numerically realize an optical spike-timing dependent plasticity (STDP) scheme by using a single vertical-cavity surface-emitting laser (VCSEL). In the scheme, the VCSEL is subjected to an orthogonally-polarized continuous-wave optical injection (OPCWOI) and dual-polarized pulsed optical injections (DPPOI). Based on the widely used spin-flip model, the response spiking dynamics of VCSEL is numerically studied, and then the optical STDP in a single VCSEL is explored. The roles of bias current, the strength of OPCWOI and DPPOI, and the frequency detuning on the optical STDP curve are numerically analyzed. It is found that, by simultaneously utilizing the response spiking dynamics in two orthogonal polarization modes, an optical STDP could be achieved by using a single VCSEL. Furthermore, the weight update of STDP curve can be calculated in real-time. Additionally, the STDP curves can also be controlled by adjusting some controllable parameters. The real-time optical STDP based on a single VCSEL is numerically realized for the first time, which paves the way towards fully VCSELs-based photonic neuromorphic systems with low power consumption.

The dynamic behavior of an optical micro ring resonator (OMRR) with an amplitude modulator positioned in the micro ring is investigated quantitatively by adopting a recently introduced quantifier, the permutation entropy (PE). The effects of modulation depth are focused on, and the roles of input power are considered. The two-dimensional (2D) maps of PE showing dependence on both modulation depth and input power are presented as well. PE values nearly increase with modulation depth. On the other hand, the optimal value of input power is achieved when the PE reaches its maximum. Thus, PE can successfully quantify the dynamics of modulated OMRR. Selecting the parameters in the region with high PE values would contribute to the complexity-enhanced OMRR-based chaotic communication systems.