Photonic reservoir computer using speckle in multimode waveguide ring resonators.

Photonic reservoir computers (RC) come in single mode ring and multimode array geometries. We propose and simulate a photonic RC architecture using speckle in a multimode waveguide ring resonator that requires neither the ultra-high-speed analog-digital conversion nor the spatial light modulator used in other designs. We show that the equations for propagation around a multimode (MM) ring resonator along with an optical nonlinearity, and optical feedback can be cast exactly in the standard RC form with speckle mixing performing the pseudo-random matrix multiplications. The hyperparameters are the outcoupling efficiency, the nonlinearity saturation intensity, the input bias, and the waveguide properties. In particular, the number of waveguide modes is a measure of the number of effective neurons in the RC. Simulations show a ring using a strongly guiding 50-µm planar waveguide gives 206 effective neurons and excellent predictions of Mackey-Glass waveforms for a broad range of the hyperparameters, while a weakly guiding MM 200-µm diameter fiber gives 4,238 effective neurons and excellent predictions of chaotic solutions of the Kuramoto-Sivashinsky equation. We discuss physical realizations for implementing the system with a chip-scale device or with discrete components and a MM optical fiber.

Classification of time-domain waveforms using a speckle-based optical reservoir computer.

Reservoir computing is a recurrent machine learning framework that expands the dimensionality of a problem by mapping an input signal into a higher-dimension reservoir space that can capture and predict features of complex, non-linear temporal dynamics. Here, we report on a bulk electro-optical demonstration of a reservoir computer using speckles generated by propagating a laser beam modulated with a spatial light modulator through a multimode waveguide. We demonstrate that the hardware can successfully perform a multivariate audio classification task performed using the Japanese vowel speakers public data set. We perform full wave optical calculations of this architecture implemented in a chip-scale platform using an SiO2 waveguide and demonstrate that it performs as well as a fully numerical implementation of reservoir computing. As all the optical components used in the experiment can be fabricated using a commercial photonic integrated circuit foundry, our result demonstrates a framework for building a scalable, chip-scale, reservoir computer capable of performing optical signal processing.