Scholarlink

<p>Microelectromechanical systems (MEMS) provide a platform where sensing, memory, and computation can co-exist within the same physical substrate. Here, we present a hysteresis-aware MEMS-based Continuous-Time Recurrent Neural Network (MEMS-CTRNN) that transforms nonlinear bistability from a device limitation into a computational resource. By embedding hysteresis into the training process, the MEMS-CTRNN achieves temporal memory and noise robustness with threefold fewer parameters than digital Long Short-Term Memory (LSTM) models, while maintaining comparable accuracy. Using insect-scale robotic flight data for validation, we demonstrate collision detection at 93% accuracy, establishing MEMS devices as viable analog recurrent processors. This framework suggests a pathway for MEMS-based neuromorphic architectures that unify sensing and computing for applications spanning microrobotics and in situ biomedical monitoring.</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

Hysteresis-aware MEMS neuromorphic networks for embedded sensing and computation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月6日

查看全文
  • A. Al zubi,
  • M. Megdadi,
  • Y. Chen,
  • M. Okour,
  • M. Al Fayad,
  • S. Mohaidat,
  • D. Lin,
  • F. Alsaleem

摘要

Microelectromechanical systems (MEMS) provide a platform where sensing, memory, and computation can co-exist within the same physical substrate. Here, we present a hysteresis-aware MEMS-based Continuous-Time Recurrent Neural Network (MEMS-CTRNN) that transforms nonlinear bistability from a device limitation into a computational resource. By embedding hysteresis into the training process, the MEMS-CTRNN achieves temporal memory and noise robustness with threefold fewer parameters than digital Long Short-Term Memory (LSTM) models, while maintaining comparable accuracy. Using insect-scale robotic flight data for validation, we demonstrate collision detection at 93% accuracy, establishing MEMS devices as viable analog recurrent processors. This framework suggests a pathway for MEMS-based neuromorphic architectures that unify sensing and computing for applications spanning microrobotics and in situ biomedical monitoring.