Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Programmable memtransistor array with temporal dynamics modulation for efficient time-series data processing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月4日

查看全文
  • Dae-won Kim,
  • Yoonho Cho,
  • Seokho Seo,
  • Yujin Kim,
  • See-On Park,
  • Taehwan Jang,
  • Chaebin Park,
  • Young Taek Oh,
  • Jae-Duk Lee,
  • Shinhyun Choi

摘要

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.