Publication date: 15th April 2025
Spin-wave reservoir computing (RC) enables effective neuromorphic computation by utilizing the spatiotemporal dynamics of spin waves in a magnetic thin film. The properties of spin-wave RC systems, governed by the Landau-Lifshitz-Gilbert (LLG) equation, can be complex and may differ from those of software-based RC systems, such as echo-state networks. Consequently, a deeper understanding of spin-wave dynamics and their role in neuromorphic computing is crucial for optimizing system design and advancing practical applications. In this work, we propose an analytic-signal-based input-output model for spin-wave RC systems, enabling direct computation of system responses without requiring physical experiments or numerical simulations, given knowledge of the system's standard responses at low input power. The model demonstrates that individual reservoir outputs capture local spin-wave responses with information encoded in two dimensions with their respective step-response functions. Building on this, we propose passband signal learning, where spin-wave responses are directly fed to the readout for learning without signal demodulation. Passband signal learning not only reduces system peripherals but also leverages the wave-based characteristics of the system's responses for more efficient information learning. We evaluate the effectiveness of this approach in mitigating multipath interference (MPI), in comparison to the conventional learning method. Our results show that spin-wave RC systems with passband signal learning more effectively address the MPI problem, owing to the inherent compatibility between spin-wave dynamics and communication channels. This work is expected to provide valuable insights into spin-wave physics and advance the development of spin-wave-based neuromorphic computing systems.
This work was supported in part by the JSPS KAKENHI under Grant No. 23H00487; in part by the Cooperative Research Project Program of the Research Institute of Electrical Communication (RIEC), Tohoku University; in part by the New Energy and Industrial Technology Development Organization (NEDO) under Project No. JPNP16007; and in part by the JSPS Core-to-Core Program (A. Advanced Research Networks) Material Intelligence: Exploiting Intrinsic Learning and Optimization Capabilities for Intelligent Systems.
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