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 5 × 5 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 sho...

中文翻译：

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开发基于5×5 VCSEL阵列的用于大脑启发式计算的光子硬件平台

像人工神经网络这样的以脑为灵感的计算概念已经成为经典冯·诺依曼计算机体系结构的有希望的替代方案。光子神经网络的目标是在光子衬底中实现神经元，网络连接以及潜在的学习。在这里，我们通过高质量的垂直腔表面发射激光器（VCSEL）阵列报告了快速高效节能的光子神经元的纳米光子硬件平台的开发。开发的5×5 VCSEL阵列通过均质的制造以及对激光波长的单独控制相结合，提供了高的光学注入锁定效率。注入锁定对于基于VCSEL的光子神经元中信息的可靠处理至关重要，我们通过注入锁定测量和电流感应光谱微调证明了VCSEL阵列的适用性。我们发现，我们研究的阵列可以很容易地调整到所需的光谱均匀性，因此应该...