Recent Developments in Photonic Integration: Reservoir Computing in Photonics using Silicon Microring Nonlinearities

The rapid evolution of integrated photonics is revolutionizing the landscape of high-speed, energy-efficient computing. One of the most promising frontiers in this domain is Photonic Reservoir Computing (PRC) , a neuromorphic paradigm that leverages the inherent dynamics and parallelism of photonic systems. This paper provides an in-depth exploration of reservoir computing implemented through nonlinear effects in silicon microring resonators (MRRs) —a scalable and CMOS-compatible platform for optical information processing. We examine the fundamental physical processes, such as two-photon absorption, free carrier dispersion, and thermal-optic phenomena, that facilitate nonlinear transformation and short-term memory essential for RC operations. Recent advances in photonic integration are surveyed, highlighting how MRR-based architectures support time-multiplexed virtual node generation and can be effectively deployed for complex signal processing tasks. A simulation framework in MATLAB is presented to model the nonlinear carrier dynamics and evaluate the RC system on a distorted QPSK signal with chromatic dispersion and Kerr nonlinearity . Results demonstrate accurate symbol recovery through a simple linear readout, validating the feasibility of silicon microrings for high-performance PRC. We further discuss emerging applications in AI-on-Chip inference engines, fiber-optic communications, and neuromorphic sensor fusion , and outline challenges and opportunities in scaling and enhancing photonic reservoir systems. This work positions silicon microring-based PRC as a compelling solution for next-generation integrated photonic computing.