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A Fingertip-Mimicking 1216 200 m-Resolution e-Skin Taxel Readout Chip With Per-Taxel Spiking Readout and Embedded

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    This study introduces a high-resolution electronic skin (e-skin) with integrated neuromorphic processing. The e-skin achieves excellent tactile sensing accuracy and state-of-the-art low power consumption for advanced robotics.

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    Area of Science:

    • Robotics and Artificial Intelligence
    • Materials Science and Engineering
    • Neuroscience and Neuromorphic Computing

    Background:

    • Traditional electronic skin (e-skin) systems face challenges in achieving high spatial resolution and efficient on-chip processing.
    • Integrating tactile sensing with neuromorphic computation is crucial for developing advanced robotic systems with human-like touch capabilities.

    Purpose of the Study:

    • To present a novel electronic skin (e-skin) taxel array readout chip with high spatial resolution and integrated neuromorphic processing.
    • To demonstrate the chip's capability in classifying tactile stimuli using Spiking Neural Networks (SNNs).
    • To evaluate the power efficiency and compare different spike encoding architectures.

    Main Methods:

    • Developed a 0.18m CMOS chip integrating a 1216 polyvinylidene fluoride (PVDF)-based piezoelectric sensor array.
    • Implemented per-taxel signal conditioning, spiking readout, and local neuromorphic processing using Complex Receptive Fields (CRFs).
    • Utilized Spiking Neural Networks (SNNs) for classifying tactile stimuli (texture, flutter frequency, indentation) and compared neuromorphic level-crossing sampling (N-LCS) with conventional level-crossing sampling (LCS).

    Main Results:

    • Achieved the highest reported spatial resolution of 200μm, comparable to human fingertips.
    • Demonstrated excellent SNN-based classification accuracies for texture (up to 97.1%), flutter frequency (up to 99.2%), and indentation (95.5%).
    • Obtained state-of-the-art power consumption (12.33nW per-taxel) and a novel N-LCS architecture outperformed conventional LCS for texture classification.

    Conclusions:

    • The developed e-skin chip offers high-resolution tactile sensing with efficient, integrated neuromorphic processing.
    • The SNN-based classification demonstrates the potential for advanced tactile perception in robotic applications.
    • The proposed N-LCS architecture provides a more efficient method for spike encoding in neuromorphic systems.