Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers

Abstract

Neuro-inspired implementations have attracted strong interest as a power efficient and robust alternative to the digital model of computation with a broad range of applications. Especially, neuro-mimetic systems able to produce and process spike-encoding schemes can offer merits like high noise-resiliency and increased computational efficiency. Towards this direction, integrated photonics can be an auspicious platform due to its multi-GHz bandwidth, its high wall-plug efficiency and the strong similarity of its dynamics under excitation with biological spiking neurons. Here, we propose an integrated all-optical neuron based on an InAs/InGaAs semiconductor quantum-dot passively mode-locked laser. The multi-band emission capabilities of these lasers allows, through waveband switching, the emulation of the excitation and inhibition modes of operation. Frequency-response effects, similar to biological neural circuits, are observed just as in a typical two-section excitable laser. The demonstrated optical building block can pave the way for high-speed photonic integrated systems able to address tasks ranging from pattern recognition to cognitive spectrum management and multi-sensory data processing.

Optoelectronic Synapse Based on IGZO‐Alkylated Graphene Oxide Hybrid Structure

“…Meanwhile, on account of changes in the physical representations of information, photonic spike processing has emerged as high‐speed, low‐power computational domain that is inaccessible by pure electronic devices . Photonic synapses based on an amorphous metal‐sulfide microfiber, graphene‐coupled laser system, and phase change material have recently been demonstrated by exploiting light as the input and output signals.…”

All-Optical Spiking Neuron Based on Passive Microresonator

“…Modulators combined with photodetectors [23,24], SOA-MZIs [19,38] and laser-cooled atoms with electro-magnetically induced transparency [25] are used to realize the nonlinear activation function in ANN. Meanwhile, lasers, e.g., micro-pillar lasers [36], two-section lasers with saturable absorber regions [26,27], vertical-cavity surface-emitting lasers (VCSELs) [34,35,37,40] and quantum dot (QD) lasers [39] are typically used to implement the functionality of spiking neurons in SNN. Owing to its unique optical properties in different states, phase change material (PCM) has emerged as an attractive alternative to provide in hardware both the basic integrate-andfire functionality of neurons and the plastic weighting operation of synapses [28][29][30][31].…”

ITO-based electro-absorption modulator for photonic neural activation function

“…Different nonlinear activation functions with significantly different ranges and trends 14 have been proposed and extensively investigated, providing suitable advantages according to the different applications. More in details, interesting experimental and numerical all-optical nonlinear module based on saturable and reverse absorption 15,16 , graphene excitable lasers 17,18 , twosection distributed-feedback (DFB) lasers 19 , quantum dots 20 , disks lasers 21,22 , induced transparency in quantum assembly 15 have recently been reported and showed promising results in terms of efficiency and throughput for different kinds of neural network and applications, ranging from convolutional neural network, spiking neural network and reservoir computing. Although, a more straightforward implementation is currently attained by exploiting electro-optic tuned nonlinear materials 23,24 or absorptive modulator directly connected to a photodiode, as shown in 14,[25][26][27][28] .…”