Developing of a photonic hardware platform for brain-inspired computing based on VCSEL arrays
arXiv:2006.13933 · doi:10.1088/2515-7647/aba671
Abstract
Brain-inspired computing concepts like artificial neural networks have become promising alternatives to classical von Neumann computer architectures. Photonic neural networks target the realizations of neurons, network connections and potentially learning in photonic substrates. Here, we report the development of a nanophotonic hardware platform of fast and energy-efficient photonic neurons via arrays of high-quality vertical cavity surface emitting lasers (VCSELs). The developed VCSEL arrays provide high optical injection locking efficiency through homogeneous fabrication combined with individual control over the laser wavelengths. Injection locking is crucial for the reliable processing of information in VCSEL-based photonic neurons, and we demonstrate the suitability of the VCSEL arrays by injection locking measurements and current-induced spectral fine-tuning. We find that our investigated array can readily be tuned to the required spectral homogeneity, and as such show that VCSEL arrays based on our technology can act as highly energy efficient and ultra-fast photonic neurons for next generation photonic neural networks. Combined with fully parallel photonic networks our substrates are promising for ultra-fast operation reaching 10s of GHz bandwidths, and we show that a nonlinear transformation based on our lasers will consume only about 100 fJ per VCSEL, which is highly competitive, compared to other platforms.
References in corpus (11)
- Deep Learning with Coherent Nanophotonic Circuits
- All-Optical Machine Learning Using Diffractive Deep Neural Networks
- Optoelectronic Reservoir Computing
- Tunable nanophotonics enabled by chalcogenide phase-change materials
- Reinforcement Learning in a large scale photonic Recurrent Neural Network
- Large-Scale Optical Neural Networks based on Photoelectric Multiplication
- Matrix optimization on universal unitary photonic devices
- Diffractive coupling for photonic networks: how big can we go?
- Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations
- Injection locking of quantum dot microlasers operating in the few photon regime
- Online training for high-performance analogue readout layers in photonic reservoir computers
Cited by in corpus (10)
- Image sensing with multilayer, nonlinear optical neural networks
- Deep Learning with Coherent VCSEL Neural Networks
- Experimental reservoir computing with diffractively coupled VCSELs
- Optical pumping of quantum dot micropillar lasers
- Injection locking and coupling the emitters of large VCSEL arrays via diffraction in an external cavity
- Multipartite entanglement generation in coupled microcavity arrays
- Enhanced Polarization Locking in VCSELs
- Advanced micropillar cavities: room-temperature operation of microlasers
- Buried Stressor Engineering for Position-Controlled InGaAs Quantum Dots with Local Density Variation for Integrated Quantum Photonics
- Room-temperature, continuous wave lasing in planar microcavities with quantum dots