An energy-efficient, CMOS-compatible physical reservoir node with post-fabrication tunable decay dynamics

Abstract

In reservoir computing, the memory decay rate of physical reservoir nodes governs how quickly past inputs fade, thereby determining their temporal dynamics. Optimising this rate is therefore crucial for effective temporal signal processing. However, in most reported physical reservoirs, it is fixed at the time of fabrication and cannot be altered on demand for different applications. Thus, tailoring a single node for its adaptability across tasks with diverse temporal characteristics remains challenging. In this work, we propose and computationally analyse a CMOS-compatible, tunable-decay, hybrid reservoir node that integrates a subthreshold-operated field-effect transistor (FET) with a programmable ReRAM device (a memristor) and a capacitor connected at its gate terminal. The reservoir output measured as the FET drain current simultaneously captures temporal memory and nonlinear transformation of the input, while the memory decay time constant (τ = R × C) can be modulated in real time by adjusting the ReRAM resistance. We demonstrate the effectiveness of the proposed node on two representative benchmark tasks with contrasting τ requirements, namely, the MNIST digit classification with 96% accuracy, and a chaotic Hénon map prediction with a normalised RMS error of 0.0037, matching state-of-the-art hardware reservoirs. Our design achieves ultra-low energy consumption (≈ 15.20pJ/operation), at least an order of magnitude lower than state-of-the-art implementations, while maintaining reliable operation and on-demand τ tunability. This combination of mature silicon technology and adaptive memristive functionality paves the way for energy-efficient, scalable, and reliable temporal learning systems. © 2025 The Author(s). Published by IOP Publishing Ltd.