Integrated programmable spectral filter for frequency-multiplexed neuromorphic computers

Artificial neural networks (ANN) are a groundbreaking technology massively em-ployed in a plethora of fields. Currently, ANNs are mostly implemented through electronic digital computers, but analog photonic implementations are very interesting mainly because of low power consumption and high bandwidth. We recently demonstrated a photonic neuromorphic computing system based on frequency multiplexing that executes ANNs algorithms as reservoir computing and Extreme Learning Machines. Neuron signals are encoded in the amplitude of the lines of a frequency comb, and neuron interconnections are realized through frequency-domain interference. Here we present an integrated programmable spectral filter designed to manipulate the optical frequency comb in our frequency multiplexing neuromorphic computing platform. The programmable filter controls the attenuation of 16 independent wavelength channels with a 20 GHz spacing. We discuss the design and the results of the chip characterization, and we preliminary demonstrate, through a numerical simulation, that the produced chip is suitable for the envisioned neuromorphic computing application.& COPY; 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Artificial neural networks (ANN) are a groundbreaking technology used in various fields. Photonic implementation of ANN algorithms using frequency multiplexing has advantages of low power consumption and high bandwidth. An integrated programmable spectral filter is designed to manipulate the optical frequency comb in our neuromorphic computing platform.