Neuromorphic Photonic Image Processing based on Temporal Coding with a Spiking VCSEL-SA Neuron

The neuromorphic photonic approaches hold great potential for developing systems with applications in information processing, computer vision, and artificial intelligence platforms. Here, we present a theoretical investigation of a neuromorphic photonic system based on a vertical cavity surface emitting laser with an embedded saturable absorber (VCSEL-SA). The primary objective of this system is to detect edge features in digital images by encoding sub-nanosecond spiking responses of the VCSEL-SA neuron. The unique attributes of the proposed technique provide an effective approach to identifying edge features by utilization of a singular kernel operator and implementation of a current ramp for the gain region. Results indicate that the system can effectively reconstruct the input image by combining the spiking response of the VCSEL-SA neuron in reaction to the injected edge patterns. Moreover, an analysis is conducted on the temporal structure, the output power, and the arrangement of spikes throughout a single cycle.