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Brain-inspired computing concepts like artificial neural networks have becomepromising alternatives to classical von Neumann computer architectures.Photonic neural networks target the realizations of neurons, networkconnections and potentially learning in photonic substrates. Here, we reportthe development of a nanophotonic hardware platform of fast andenergy-efficient photonic neurons via arrays of high-quality vertical cavitysurface emitting lasers (VCSELs). The developed $5\times5$ VCSEL arrays providehigh optical injection locking efficiency through homogeneous fabricationcombined with individual control over the laser wavelengths. Injection lockingis crucial for the reliable processing of information in VCSEL-based photonicneurons, and we demonstrate the suitability of the VCSEL arrays by injectionlocking measurements and current-induced spectral fine-tuning. We find that ourinvestigated array can readily be tuned to the required spectral homogeneity,and as such show that VCSEL arrays based on our technology can act as highlyenergy efficient and ultra-fast photonic neurons for next generation photonicneural networks. Combined with fully parallel photonic networks our substratesare promising for ultra-fast operation reaching 10s of GHz bandwidths, and weshow that a nonlinear transformation based on our lasers will consume onlyabout 100 fJ per VCSEL, which is highly competitive, compared to otherplatforms.

Developing of a photonic hardware platform for brain-inspired computing based on $5\times5$ VCSEL arrays