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Programmable memtransistor array with temporal dynamics modulation for efficient time-series data processing
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DOI：10.1038/s41467-026-75211-5.png)
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Abstract
En 中文
Hardware-based reservoir computing systems promise energy-efficient time-series data processing. However, their non-adjustable temporal dynamics at the hardware level limit multiscale feature extraction and computing capacity. Here, we present a programmable dynamic memtransistor featuring a dual-functional gate stack that enables hardware-level control of dynamic behavior without constant biasing or input preprocessing. The memtransistor integrates a charge storage layer for volatile temporal processing and a charge trap layer for non-volatile modulation of the band structure, enabling a 5-fold tunability in relaxation time constants. By configuring memtransistors with distinct dynamics in parallel, we realize a wide reservoir computing system capable of processing signals across multiple timescales. This architecture delivers a 40-fold reduction in the error for the multiple superimposed oscillator prediction task compared to a single reservoir baseline, and achieves software-comparable accuracy in forecasting the chaotic Lorenz attractor. Our results establish a compact and energy-efficient hardware platform for scalable wide reservoir computing implementation. Tunable temporal dynamics are needed for efficient hardware processing of time-series data. Here, the authors demonstrate a memtransistor array that non-volatilely tunes device dynamics for multiscale signal processing without extra circuitry.
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