Hong Seok Ju, Lee Yu Rim, Bag Atanu, Kim Hyo Soo, Trung Tran Quang, Sultan M Junaid, Moon Dong-Bin, Lee Nae-Eung
School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
Research Center for Advanced Materials Technology, Sungkyunkwan University, Suwon, Republic of Korea.
Nat Mater. 2025 Apr 28. doi: 10.1038/s41563-025-02204-y.
Tactile perception involves the preprocessing of signals from slowly adapting and fast-adapting afferent neurons, which exhibit synapse-like interactions between mechanoreceptors and their dendrites or terminals, transmitting signals to the brain. Emulating these adaptation and sensory memory functions is crucial for artificial tactile sensing systems. Here, inspired by human tactile afferent systems, we present an array of artificial synaptic mechanoreceptors with built-in synaptic functions by vertically integrating synaptic transistors with a reduced graphene oxide channel, an ionogel gate dielectric and an elastomeric fingerprint-like receptive layer in an all-in-one platform. Triboelectric-capacitive gating between the receptive layer and gate dielectric in response to tactile stimulation governs excitatory post-synaptic current patterns, enabling slowly adapting and fast-adapting characteristics for signal preprocessing. The artificial synaptic mechanoreceptor array demonstrated handwriting style, surface pattern and texture discrimination via machine learning using fused slowly adapting and fast-adapting post-synaptic values, offering high data efficiency and potential for intelligent skin.
触觉感知涉及对来自慢适应和快适应传入神经元的信号进行预处理，这些神经元在机械感受器与其树突或末梢之间表现出类似突触的相互作用，将信号传输到大脑。模拟这些适应和感觉记忆功能对于人工触觉传感系统至关重要。在此，受人类触觉传入系统的启发，我们通过在一个一体化平台中将突触晶体管与还原氧化石墨烯通道、离子凝胶栅极电介质和弹性体指纹状感受层垂直集成，展示了一种具有内置突触功能的人工突触机械感受器阵列。响应触觉刺激，感受层与栅极电介质之间的摩擦电容门控控制兴奋性突触后电流模式，实现信号预处理的慢适应和快适应特性。人工突触机械感受器阵列通过使用融合的慢适应和快适应突触后值进行机器学习，展示了手写风格、表面图案和纹理辨别能力，具有高数据效率和智能皮肤的潜力。
