Physical Reservoir Computing is a recently developed form of Neuromorphic Computing where a non-linear physical system transfers data to higher dimensions. This information is used to train linear weights digitally to perform a specific task. Although various physical hardware have shown promising results for reservoir computing, it is not certain how to build a suitable reservoir given a set of physical components to perform a specific task. In this work, we study signal dynamics and information processing in a hybrid photonic electrical reservoir and how it can be used to build an optimal reservoir.
Ultrafast modulation, switching and optical wave mixing are essential functionalities for various CMOS compatible photonic integrated circuits with applications for optical communication, signal processing and computing. We explore second and third order nonlinear susceptibilities of various inhomogeneous thin film compositions and characterize their properties using a Maker fringes setup and ultrashort femtosecond scale laser pulses. Specifically, we demonstrate enhanced effective second order nonlinear response by engineering the compositions to create a strong internal DC electric fields (~20 pm/V) as well as synthesizing silicon rich silicon nitride films with high second order nonlinear polarizability (~8 pm/V) in as grown films.
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