Research Progress of Photonic Spiking Neural Networks (Invited)

Yahui Zhang; Shuiying Xiang; Xingxing Guo; Yanan Han; Changjian Xie; Tao Wang; Yue Hao

doi:10.3788/LOP251019

Laser & Optoelectronics Progress, Volume. 62, Issue 17, 1739011(2025)

Research Progress of Photonic Spiking Neural Networks (Invited)

Yahui Zhang^1,2, Shuiying Xiang^1,2、*, Xingxing Guo^1,2, Yanan Han^1,2, Changjian Xie¹, Tao Wang¹, and Yue Hao²

¹State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi'an 710071, Shaanxi , China

²State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, Shaanxi , China

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Figures & Tables(13)

Fig. 1. Circuit diagram of LIF neuron

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Fig. 2. Schematic diagrams of encoding method^[23]

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Fig. 3. Photonic spiking neurons with different material systems and device structures^[26-34]

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Fig. 4. Simulation results of neuron-like properties of two-section semiconductor lasers. (a) Neuron-like properties of the classical two-section semiconductor laser model^[29]; (b) neuron-like properties of the time-dependent traveling wave model^[38]; (c) intrinsic plasticity and activation-weight integration properties of the time-dependent traveling wave model^[39]

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Fig. 5. Simulation results of neuron-like properties of VCSEL-based photonic spiking neuron based on spin inversion model. (a) Spike properties^[40]; (b) simulation of tonic and phasic spikes^[39]

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Fig. 6. Implementation schemes of partial optical spiking neurons based on commercial devices. (a) VCSEL photonic spiking neuron^[46]; (b) DFB photonic spiking neuron^[41]; (c) FP photonic spiking neuron^[54]; (d) dual-IQ modulator photonic spiking neuron^[55]

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Fig. 7. Microscope images of partial optical spiking neuron chips. (a) MRR^[34]; (b) FP-SA^[32]; (c) DFB-SA^[36]

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Fig. 8. Models and architectures of partial photonic spiking neural networks. (a) In-memory computing architecture based on phase-change materials^[72]; (b) spiking neural network computing architecture based on all-VCSEL^[75]; (c) connection between two-layer FP-SA photonic spiking neurons^[79]; (d) hybrid integrated photonic spiking neural network based on 4×4 MZI array and VCSELs-SA^[77]

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Fig. 9. Integrated chips of partial photonic spiking neural networks. (a) Spiking neural network chip based on PCM^[64]; (b) photonic spiking convolutional neural network array chip^[37]

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Table 1. Partial parameters of VCSEL-SA

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Table 1. Partial parameters of VCSEL-SA

Parameters	Values in gain section	Values in absorber section
Cavity volume $V$	$2.4 \times 10^{- 18} m^{3}$	$2.4 \times 10^{- 18} m^{3}$
Confinement factor $Γ$	0.06	0.05
Carrier lifetime $τ$	1 ns	100 ps
Differential gain/loss $g$	$2.9 \times 10^{- 12} m^{3} s^{- 1}$	$14.5 \times 10^{- 12} m^{3} s^{- 1}$
Transparency carrier density $n_{0}$	$1.1 \times 10^{24} m^{- 3}$	$0.89 \times 10^{24} m^{- 3}$
Lasing wavelength $λ$	850 nm
Speed of light $c$	$3 \times 10^{8} m s^{- 1}$
Bimolecular recombination $B_{r}^{}$	$10 \times 10^{- 16} m^{3} s^{- 1}$
Spontaneous emission coupling factor $β$	$1 \times 10^{- 4}$
Output power coupling coefficient $η_{c}$	0.4
Photon lifetime $τ_{p h}$	4.8 ps
Planck constant $h$	$6.63 \times 10^{- 34}$ Js

Table 2. Partial parameters of time-dependent traveling wave model

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Table 2. Partial parameters of time-dependent traveling wave model

Parameter	Value	Parameter	Value
Grating coupling coefficient $κ$	1000 m^-1	Confinement factor $Γ$	1
Linear recombination coefficient $A$	$1 \times 10^{8}$ s^-1	Internal loss $α_{s}$	5000 m^-1
Bimolecular recombination coefficient $B$	$1 \times 10^{- 16}$ m³/s	Length of the laser cavity $L$	300 $μ m$
Auger recombination coefficient $C$	$3.5 \times 10^{- 41}$ m³/s	Length of gain section $L_{G}$	280 $μ m$
Effective refractive index $n_{e f f}$	3.2	Length of SA section $L_{S A}$	20 $μ m$
Linewidth enhancement factor $α_{m}$	$1.5 \times 10^{24}$	Gain suppression coefficient $ε$	$6 \times 10^{- 23} m^{3}$
spontaneous emission factor $n_{s p}$	$5 \times 10^{- 5}$	Transparent carrier density $N_{T}$	3.6

Table 3. Dynamical properties of photonic spiking neurons for partial commercial devices

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Table 3. Dynamical properties of photonic spiking neurons for partial commercial devices

Device	Threshold	Technical route	Function
VCSEL^［43-50］	1‒3 mA	Intensity perturbation，frequency switching，electronic perturbation	Spike coding， edge detection， firing threshold， temporal integration， refractory period
DFB^［51-53］	10‒50 mA	Intensity perturbation	Spike coding， radio frequency signal processing， firing threshold， temporal integration， refractory period
Dual-IQ modulation^［54］		Intensity， phase-encoding	Linear photonic neuron
FP Laser^［55］	~10 mA	Intensity perturbation	Ultra-fast obstacle avoidance
Quantum dot laser^［56-58］	~200 mA	Intensity perturbation， electronic perturbation	Spike coding， firing threshold

Table 4. Integrated chips of photonic spiking neurons

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Table 4. Integrated chips of photonic spiking neurons

Chip	Material/wavelength	Spike rate /GHz	Energy spike	Area
BPD+MRR^［59］	Si/1550 nm			8 μm×50 μm， 11.5 μm（diameter）
MRR+PCM^［30］	Si/1550 nm	0.025	0.7 pJ	100 μm²
MRR^［34］	Si/1550 nm	0.25	3.528 pJ	~1575 μm²
Micropillar laser^［28］	InP/980 nm	0.082	50‒700 fJ	4 μm（diameter）
DFB + BPD^［31］	InP/1550 nm	2	10.4 pJ	~0.055 mm²
FP-SA^［32］	InP/1550 nm	3.3	7.239 fJ	300 μm×1500 μm
DFB-SA^［36］	InP/1550 nm	5	19.99 fJ	250 μm×300 μm
Opto-electronic neuron^［34］	CMOS/1310 nm	1	1.18 pJ	~4 cm²

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Yahui Zhang, Shuiying Xiang, Xingxing Guo, Yanan Han, Changjian Xie, Tao Wang, Yue Hao. Research Progress of Photonic Spiking Neural Networks (Invited)[J]. Laser & Optoelectronics Progress, 2025, 62(17): 1739011

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Paper Information

Category: AI for Optics

Received: Apr. 16, 2025

Accepted: Jun. 19, 2025

Published Online: Sep. 12, 2025

The Author Email: Shuiying Xiang (syxiang@xidian.edu.cn)

DOI:10.3788/LOP251019

CSTR:32186.14.LOP251019

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology