RESUMO
On-chip microring resonators (MRRs) have been proposed to construct time-delayed reservoir computing (RC) systems, which offer promising configurations available for computation with high scalability, high-density computing, and easy fabrication. A single MRR, however, is inadequate to provide enough memory for the computation task with diverse memory requirements. Large memory requirements are satisfied by the RC system based on the MRR with optical feedback, but at the expense of its ultralong feedback waveguide. In this paper, a time-delayed RC is proposed by utilizing a silicon-based nonlinear MRR in conjunction with an array of linear MRRs. These linear MRRs possess a high quality factor, providing enough memory capacity for the RC system. We quantitatively analyze and assess the proposed RC structure's performance on three classical tasks with diverse memory requirements, i.e., the Narma 10, Mackey-Glass, and Santa Fe chaotic timeseries prediction tasks. The proposed system exhibits comparable performance to the system based on the MRR with optical feedback, when it comes to handling the Narma 10 task, which requires a significant memory capacity. Nevertheless, the dimension of the former is at least 350 times smaller than the latter. The proposed system lays a good foundation for the scalability and seamless integration of photonic RC.