We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback(DFB)laser chip with an intracavity saturable absorber(SA).The DFB laser with an intracavity SA(DFB...We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback(DFB)laser chip with an intracavity saturable absorber(SA).The DFB laser with an intracavity SA(DFBSA)contains a gain region and an SA region.The gain region is designed and fabricated by the asymmetric equivalentπ-phase shift based on the reconstruction-equivalent-chirp technique.Under properly injected current in the gain region and reversely biased voltage in the SA region,periodic self-pulsation was experimentally observed due to the Q-switching effect.The self-pulsation frequency increases with the increase of the bias current and is within the range of several gigahertz.When the bias current is below the self-pulsation threshold,neuronlike spiking responses appear when external optical stimulus pulses are injected.Experimental results show that the spike threshold,temporal integration,and refractory period can all be observed in the fabricated DFB-SA chip.To numerically verify the experimental findings,a time-dependent coupled-wave equation model was developed,which described the physics processes inside the gain and SA regions.The numerical results agree well with the experimental measurements.We further experimentally demonstrated that the weighted sum output can readily be encoded into the self-pulsation frequency of the DFB-SA neuron.We also benchmarked the handwritten digit classification task with a simple single-layer fully connected neural network.By using the experimentally measured dependence of the self-pulsation frequency on the bias current in the gain region as an activation function,we can achieve a recognition accuracy of 92.2%,which bridges the gap between the continuous valued artificial neural networks and spike-based neuromorphic networks.To the best of our knowledge,this is the first experimental demonstration of a photonic integrated spiking neuron based on a DFB-SA,which shows great potential to realizing large-scale multiwavelength photonic spiking neural network chi展开更多
As Moore’s law has reached its limits,it is becoming increasingly difficult for traditional computing architectures to meet the demands of continued growth in computing power.Photonic neural computing has become a pr...As Moore’s law has reached its limits,it is becoming increasingly difficult for traditional computing architectures to meet the demands of continued growth in computing power.Photonic neural computing has become a promising approach to overcome the von Neuman bottleneck.However,while photonic neural networks are good at linear computing,it is difficult to achieve nonlinear computing.Here,we propose and experimentally demonstrate a coherent photonic spiking neural network consisting of Mach–Zehnder modulators(MZMs)as the synapse and an integrated quantum-well Fabry–Perot laser with a saturable absorber(FP-SA)as the photonic spiking neuron.Both linear computation and nonlinear computation are realized in the experiment.In such a coherent architecture,two presynaptic signals are modulated and weighted with two intensity modulation MZMs through the same optical carrier.The nonlinear neuron-like dynamics including temporal integration,threshold,and refractory period are successfully demonstrated.Besides,the effects of frequency detuning on the nonlinear neuron-like dynamics are also explored,and the frequency detuning condition is revealed.The proposed hardware architecture plays a foundational role in constructing a large-scale coherent photonic spiking neural network.展开更多
Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions...Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions of a photonic spiking neural network(PSNN).However,they are separately implemented with different photonic materials and devices,hindering the large-scale integration of PSNN.Here,we propose,fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spike activation based on a distributed feedback(DFB)laser with a saturable absorber(DFB-SA).A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation.Furthermore,a fourchannel DFB-SA laser array is fabricated for realizing matrix convolution of a spiking convolutional neural network,achieving a recognition accuracy of 87%for the MNIST dataset.The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.展开更多
Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuro...Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuromorphic computing.Here,we proposed a multi-synaptic photonic SNN,combining the modified remote supervised learning with delayweight co-training to achieve pattern classification.The impact of multi-synaptic connections and the robustness of the network were investigated through numerical simulations.In addition,the collaborative computing of algorithm and hardware was demonstrated based on a fabricated integrated distributed feedback laser with a saturable absorber(DFB-SA),where 10 different noisy digital patterns were successfully classified.A functional photonic SNN that far exceeds the scale limit of hardware integration was achieved based on time-division multiplexing,demonstrating the capability of hardware-algorithm co-computation.展开更多
Global climate changes,such as extreme weather,sea levels rise,and biodiversity decline,have destructive impacts on the global agrofood system,in terms of crop yield,agrofood quality and consumer safety.Those stressed...Global climate changes,such as extreme weather,sea levels rise,and biodiversity decline,have destructive impacts on the global agrofood system,in terms of crop yield,agrofood quality and consumer safety.Those stressed conditions and environmental pollution for crop and agrofood prompt us to take immediate action to develop a whole-new-era agrofood system from the perspective of sustainability[1].展开更多
Dendrites,branches of neurons that transmit signals between synapses and soma,play a vital role in spiking information processing,such as nonlinear integration of excitatory and inhibitory stimuli.However,the investig...Dendrites,branches of neurons that transmit signals between synapses and soma,play a vital role in spiking information processing,such as nonlinear integration of excitatory and inhibitory stimuli.However,the investigation of nonlinear integration of dendrites in photonic neurons and the fabrication of photonic neurons including dendritic nonlinear integration in photonic spiking neural networks(SNNs)remain open problems.Here,we fabricate and integrate two dendrites and one soma in a single Fabry–Perot laser with an embedded saturable absorber(FP-SA)neuron to achieve nonlinear integration of excitatory and inhibitory stimuli.Note that the two intrinsic electrodes of the gain section and saturable absorber(SA)section in the FP-SA neuron are defined as two dendrites for two ports of stimuli reception,with one electronic dendrite receiving excitatory stimulus and the other receiving inhibitory stimulus.The stimuli received by two electronic dendrites are integrated non-linearly in a single FP-SA neuron,which generates spikes for photonic SNNs.The properties of frequency encoding and spatiotemporal encoding are investigated experimentally in a single FP-SA neuron with two electronic dendrites.For SNNs equipped with FP-SA neurons,the range of weights between presynaptic neurons and postsynaptic neurons is varied from negative to positive values by biasing the gain and SA sections of FP-SA neurons.Compared with SNN with all-positive weights realized by only biasing the gain section of photonic neurons,the recognition accuracy of Iris flower data is improved numerically in SNN consisting of FP-SA neurons.The results show great potential for multi-functional integrated photonic SNN chips.展开更多
In this paper, we present the finding that periodic structural defects(PSDs) along a Bragg grating can shift the Bragg wavelength. This effect is theoretically analyzed and confirmed by numerical calculation. We find ...In this paper, we present the finding that periodic structural defects(PSDs) along a Bragg grating can shift the Bragg wavelength. This effect is theoretically analyzed and confirmed by numerical calculation. We find that the Bragg wavelength shift is determined by the defect size and the period of the defects. The Bragg wavelength can be well tuned by properly designing the PSDs, and this may provide an alternative method to fabricate grating-based multiwavelength devices, including optical filter arrays and laser arrays. In regards to wavelength precision, the proposed method has an advantage over the traditional methods, where the Bragg wavelengths are changed directly by changing the grating period. In addition, the proposed method can maintain grating strength when tuning the wavelength since only the period of defects is changed. This will be a benefit for devices such as arrays.展开更多
In SiC（f）/Ti-6Al-4V composites, the microstructure of the matrix close to the fiber was different from that of the fiber-less material. Microstructure observations show that a layer of fine grains was located adjace...In SiC（f）/Ti-6Al-4V composites, the microstructure of the matrix close to the fiber was different from that of the fiber-less material. Microstructure observations show that a layer of fine grains was located adjacent to the fiber, and more dislocations and faults were found in this region. Higher recrystallization nucleation rate due to the undeformed SiC fiber and thermal residual stress induced during cooling from the fabrication temperature caused the microstructural changes of the matrix. Hardness measurement indicates that the matrix in the fiber neighborhood was strengthened, and the strengthening effect decreased with distance away from the fiber.展开更多
基金National Key Research and Development Program of China(2021YFB2801900,2021YFB2801902,2021YFB2801904,2018YFE0201200)National Outstanding Youth Science Fund of National Natural Science Foundation of China(62022062)+1 种基金National Natural Science Foundation of China(61974177)Fundamental Research Funds for the Central Universities(QTZX23041)。
文摘We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback(DFB)laser chip with an intracavity saturable absorber(SA).The DFB laser with an intracavity SA(DFBSA)contains a gain region and an SA region.The gain region is designed and fabricated by the asymmetric equivalentπ-phase shift based on the reconstruction-equivalent-chirp technique.Under properly injected current in the gain region and reversely biased voltage in the SA region,periodic self-pulsation was experimentally observed due to the Q-switching effect.The self-pulsation frequency increases with the increase of the bias current and is within the range of several gigahertz.When the bias current is below the self-pulsation threshold,neuronlike spiking responses appear when external optical stimulus pulses are injected.Experimental results show that the spike threshold,temporal integration,and refractory period can all be observed in the fabricated DFB-SA chip.To numerically verify the experimental findings,a time-dependent coupled-wave equation model was developed,which described the physics processes inside the gain and SA regions.The numerical results agree well with the experimental measurements.We further experimentally demonstrated that the weighted sum output can readily be encoded into the self-pulsation frequency of the DFB-SA neuron.We also benchmarked the handwritten digit classification task with a simple single-layer fully connected neural network.By using the experimentally measured dependence of the self-pulsation frequency on the bias current in the gain region as an activation function,we can achieve a recognition accuracy of 92.2%,which bridges the gap between the continuous valued artificial neural networks and spike-based neuromorphic networks.To the best of our knowledge,this is the first experimental demonstration of a photonic integrated spiking neuron based on a DFB-SA,which shows great potential to realizing large-scale multiwavelength photonic spiking neural network chi
基金National Key Research and Development Program of China(2021YFB2801900,2021YFB2801901,2021YFB2801902,2021YFB2801904)National Natural Science Foundation of China(61974177,61674119)+1 种基金Outstanding Youth Science Fund of National Natural Science Foundation of China(62022062)Fundamental Research Funds for the Central Universities(JB210114)。
文摘As Moore’s law has reached its limits,it is becoming increasingly difficult for traditional computing architectures to meet the demands of continued growth in computing power.Photonic neural computing has become a promising approach to overcome the von Neuman bottleneck.However,while photonic neural networks are good at linear computing,it is difficult to achieve nonlinear computing.Here,we propose and experimentally demonstrate a coherent photonic spiking neural network consisting of Mach–Zehnder modulators(MZMs)as the synapse and an integrated quantum-well Fabry–Perot laser with a saturable absorber(FP-SA)as the photonic spiking neuron.Both linear computation and nonlinear computation are realized in the experiment.In such a coherent architecture,two presynaptic signals are modulated and weighted with two intensity modulation MZMs through the same optical carrier.The nonlinear neuron-like dynamics including temporal integration,threshold,and refractory period are successfully demonstrated.Besides,the effects of frequency detuning on the nonlinear neuron-like dynamics are also explored,and the frequency detuning condition is revealed.The proposed hardware architecture plays a foundational role in constructing a large-scale coherent photonic spiking neural network.
基金financial supports from National Key Research and Development Program of China (2021YFB2801900,2021YFB2801901,2021YFB2801902,2021YFB2801904)National Natural Science Foundation of China (No.61974177)+1 种基金National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (62022062)The Fundamental Research Funds for the Central Universities (QTZX23041).
文摘Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions of a photonic spiking neural network(PSNN).However,they are separately implemented with different photonic materials and devices,hindering the large-scale integration of PSNN.Here,we propose,fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spike activation based on a distributed feedback(DFB)laser with a saturable absorber(DFB-SA).A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation.Furthermore,a fourchannel DFB-SA laser array is fabricated for realizing matrix convolution of a spiking convolutional neural network,achieving a recognition accuracy of 87%for the MNIST dataset.The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.
基金supports from the National Key Research and Development Program of China (Nos.2021YFB2801900,2021YFB2801901,2021YFB2801902,2021YFB2801903,2021YFB2801904)the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (No.62022062)+1 种基金the National Natural Science Foundation of China (No.61974177)the Fundamental Research Funds for the Central Universities (No.QTZX23041).
文摘Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuromorphic computing.Here,we proposed a multi-synaptic photonic SNN,combining the modified remote supervised learning with delayweight co-training to achieve pattern classification.The impact of multi-synaptic connections and the robustness of the network were investigated through numerical simulations.In addition,the collaborative computing of algorithm and hardware was demonstrated based on a fabricated integrated distributed feedback laser with a saturable absorber(DFB-SA),where 10 different noisy digital patterns were successfully classified.A functional photonic SNN that far exceeds the scale limit of hardware integration was achieved based on time-division multiplexing,demonstrating the capability of hardware-algorithm co-computation.
基金supported by the National Key Research and Development Program of China(2018YFE0127000)the National Natural Science Foundation of China(21675127 and 31972150)+3 种基金Shaanxi Provincial Science and Technology Innovation Team(2023-CX-TD-55)the Key Industries Innovation Chain Project of Shaanxi Province(2019ZDLSF07-08)the Natural Science Foundation Project in Guangdong Province(2020A1515010778)Qinghai Provincial Key Laboratory of Qinghai-Tibet Plateau Biological Resource(2020-ZJ-T05).
文摘Global climate changes,such as extreme weather,sea levels rise,and biodiversity decline,have destructive impacts on the global agrofood system,in terms of crop yield,agrofood quality and consumer safety.Those stressed conditions and environmental pollution for crop and agrofood prompt us to take immediate action to develop a whole-new-era agrofood system from the perspective of sustainability[1].
基金National Key Research and Development Program of China(2021YFB2801900,2021YFB2801902,2021YFB2801904)National Natural Science Foundation of China(61974177,61674119,62204196,62205258)+1 种基金National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(62022062)Fundamental Research Funds for the Central Universities(QTZX23041,XJS220124)。
文摘Dendrites,branches of neurons that transmit signals between synapses and soma,play a vital role in spiking information processing,such as nonlinear integration of excitatory and inhibitory stimuli.However,the investigation of nonlinear integration of dendrites in photonic neurons and the fabrication of photonic neurons including dendritic nonlinear integration in photonic spiking neural networks(SNNs)remain open problems.Here,we fabricate and integrate two dendrites and one soma in a single Fabry–Perot laser with an embedded saturable absorber(FP-SA)neuron to achieve nonlinear integration of excitatory and inhibitory stimuli.Note that the two intrinsic electrodes of the gain section and saturable absorber(SA)section in the FP-SA neuron are defined as two dendrites for two ports of stimuli reception,with one electronic dendrite receiving excitatory stimulus and the other receiving inhibitory stimulus.The stimuli received by two electronic dendrites are integrated non-linearly in a single FP-SA neuron,which generates spikes for photonic SNNs.The properties of frequency encoding and spatiotemporal encoding are investigated experimentally in a single FP-SA neuron with two electronic dendrites.For SNNs equipped with FP-SA neurons,the range of weights between presynaptic neurons and postsynaptic neurons is varied from negative to positive values by biasing the gain and SA sections of FP-SA neurons.Compared with SNN with all-positive weights realized by only biasing the gain section of photonic neurons,the recognition accuracy of Iris flower data is improved numerically in SNN consisting of FP-SA neurons.The results show great potential for multi-functional integrated photonic SNN chips.
基金supported by the National Natural Science Foundation of China(Youth)(61306068)the Natural Science Foundation of Jiangsu Province of China(BK20130585,BK20140414)+1 种基金the National Natural Science Foundation of China(61435014,61504170,61504058)the National 863 Program(2015AA016902)
文摘In this paper, we present the finding that periodic structural defects(PSDs) along a Bragg grating can shift the Bragg wavelength. This effect is theoretically analyzed and confirmed by numerical calculation. We find that the Bragg wavelength shift is determined by the defect size and the period of the defects. The Bragg wavelength can be well tuned by properly designing the PSDs, and this may provide an alternative method to fabricate grating-based multiwavelength devices, including optical filter arrays and laser arrays. In regards to wavelength precision, the proposed method has an advantage over the traditional methods, where the Bragg wavelengths are changed directly by changing the grating period. In addition, the proposed method can maintain grating strength when tuning the wavelength since only the period of defects is changed. This will be a benefit for devices such as arrays.
文摘In SiC（f）/Ti-6Al-4V composites, the microstructure of the matrix close to the fiber was different from that of the fiber-less material. Microstructure observations show that a layer of fine grains was located adjacent to the fiber, and more dislocations and faults were found in this region. Higher recrystallization nucleation rate due to the undeformed SiC fiber and thermal residual stress induced during cooling from the fabrication temperature caused the microstructural changes of the matrix. Hardness measurement indicates that the matrix in the fiber neighborhood was strengthened, and the strengthening effect decreased with distance away from the fiber.
