Proposed for analog computing, multimode fibers have limitations due to slow spatial-domain encoding. Our work showcases instead the computational prowess of a scheme employing a step-index few-mode fiber (FMF) segment, for high-speed spatiotemporal coincidence detection by leveraging the FMF’s dispersive optical properties. The FMF is a custom-made fabrication, with NA = 0.15, a core diameter of 22 μm, and a length of 13 m, introducing delay to temporal input pulses through the supported propagation of higher-order fiber modes. The temporal mixing of these modes creates short-term memory for time-encoded information which we exploit for coincidence detection. By slightly misaligning the input beam with the FMF’s longitudinal axis, we can modify the impact of the different modes on the overall spatial pattern distribution. Our experimental system operates at 1550 nm and encodes 6-bit header patterns with 35.1 ps pulses per bit. With four distinct 40 GHz photodetected points at the output speckle pattern of the FMF, we capture four different time series that correspond to different power integrals and use them to train a logistic regression classifier. Eventually, every header classification is performed with the sampling of only one pulse time window, thus our system operates at 28.5 Gb/s. Remarkably, under various input misalignment conditions, our system demonstrates error rates below 1/5000. This level of performance could not be obtained with a standard step-index multimode fiber of the appropriate length.
We show the computational power of few-mode fibers (FMF) in a 40 Gbps spatiotemporal coincidence detector scheme. We consider a 5.5 m step-index FMF, with a 16.6 μm core diameter, as the medium that introduces various delays to a temporal input pulse, via the supported propagation fiber modes. In our representation, the different group velocities of the excited fiber modes define equivalent optical dendritic branches. A 1550 nm laser’s optical output is modulated by a 40 Gbps binary sequence and coupled to the FMF. The output optical pattern is photodetected by a 3×3 array and used to solve successfully a 6-bit header classification task.
Time-delay Reservoir Computing (TRC) are neural networks which thanks to their hardware simplicity are suited for photonic implementation. Here, we numerically investigated and experimentally tested a TRC based on a nonlinear single silicon microring resonator (MRR) coupled to an optical fibre loop which provides an external tuneable optical feedback. The work is inspired by the key role that MRRs demonstrate in integrated optical devices, together with the significant advantage they provide due to their fully passive nonlinearity. Insights about the computational properties of the system are provided as a result of the performance on multiple benchmark tasks.
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