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Because of the von Neumann bottleneck, neuromorphic networks aimed at in-memory computing, such as brains, are extensively studied. As artificial synapses are essential in neuromorphic networks, a photonic synapse based on slot-ridge waveguides with nonvolatile phase-change materials (PCMs) was proposed and demonstrated in an SOI platform with standard complementary metal-oxide-semiconductor (CMOS) process for a larger weight dynamic range. The change of the optical transmission spectrum of our photonic synapses was about 3.5dB higher than that of primitive synapses, which meant large weight dynamic range and more weight values. A 90.7% recognition accuracy based on our photonic synapses, which was 2.6% higher than that of primitive synapses, was realized for the MNIST handwritten digits recognition task performed by a three-layer perceptron. Besides, because of the nonvolatile nature of PCMs, the weights achieved by our photonic synapses can be stored in situ ensuring a lower consumption in in-memory computing. This framework can potentially achieve a more efficient in-memory computing neuromorphic network in silicon photonics.

On-Chip Photonic Synapses Based on Slot-Ridge Waveguides With PCMs For In-Memory Computing