Reservoir computing on coupled magnetic tunneling junction for time series data preprocessing

Abstract Physical reservoir computing using magnetic tunneling junction leverages the inherent nonlinearity of physical systems for computation, offering advantages such as low energy consumption and low hardware overhead for the time-series data preprocessing. However, parallelism and multiple-signal processing abilities remain challenges for it. In this work, we utilized the synchronized oscillations between coupling magnetic tunneling junction with its spin-transfer torque oscillators (STOs) effect as a nonlinear dynamical resource to construct the physical reservoir computing (PRC) unit. Unlike traditional time-multiplexing methods, this network directly processes information through the voltage amplitude and frequency dynamics of magnetic coupled STO units. The results demonstrate that this architecture achieves accuracy and memory capacity in classification tasks compared to existing solutions. Furthermore, we also used it for aeromagnetic compensation. The results show that it significantly enhances the compensation accuracy and network convergence speed. This work validates the potential of magnetic tunneling junction for high-performance parallel computing and provides an innovative path for the design of low-power neuromorphic hardware.