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<p>Numerous attempts have been made to emulate the skin’s multimodal capabilities using different device architectures, but most suffer from slow response due to reactive components and limited scalability from stacking multiple elements, which restricts their practical use. Here we report a multimodal receptor based on a single memristive nanowire network that captures both thermal and mechanical properties of interacting objects through memristive switching. The device switches between thermal and mechanical sensing at 16 Hz, whereas its intrinsic response times reach submicrosecond (mechanical) and millisecond (thermal) levels due to the nanoscale thickness. To demonstrate practicality, we integrated the receptor with a wireless switching board for daily use, combined it with a machine learning model to identify 20 household objects with 83% accuracy using a single fingertip-mounted sensor, and performed multiarray measurements for spatially distributed sensing. This approach highlights the potential of memristive networks for compact and versatile multimodal sensing in wearable and interactive devices.</p>

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Unisensory processing of interleaving memristive nanowires enabling multimodal sensing at human-scale resolution

  • 期刊论文

  • 发表日期：

    2025年10月24日

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  • Kyun Kyu Kim,
  • Junhyuk Bang,
  • Munju Kim,
  • Juho Jeong,
  • Inho Ha,
  • Seung Hwan Ko

摘要

Numerous attempts have been made to emulate the skin’s multimodal capabilities using different device architectures, but most suffer from slow response due to reactive components and limited scalability from stacking multiple elements, which restricts their practical use. Here we report a multimodal receptor based on a single memristive nanowire network that captures both thermal and mechanical properties of interacting objects through memristive switching. The device switches between thermal and mechanical sensing at 16 Hz, whereas its intrinsic response times reach submicrosecond (mechanical) and millisecond (thermal) levels due to the nanoscale thickness. To demonstrate practicality, we integrated the receptor with a wireless switching board for daily use, combined it with a machine learning model to identify 20 household objects with 83% accuracy using a single fingertip-mounted sensor, and performed multiarray measurements for spatially distributed sensing. This approach highlights the potential of memristive networks for compact and versatile multimodal sensing in wearable and interactive devices.