Park See-On, Jeong Hakcheon, Seo Seokho, Kwon Youna, Lee Jongwon, Choi Shinhyun
School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Nano Convergence Technology Division, National Nanofab Center (NNFC), Daejeon, Republic of Korea.
Nat Commun. 2025 Jul 1;16(1):5754. doi: 10.1038/s41467-025-60818-x.
The sensory nervous system in animals enables the perception of external stimuli. Developing an artificial sensory nervous system has been widely conducted to realize neuro-inspired robots capable of effectively responding to external stimuli. However, it remains challenging to develop artificial sensory nervous systems that possess sophisticated biological functions, such as habituation and sensitization, enabling efficient responses without bulky peripheral circuitry. Here, we introduce a memristor device with third-order switching complexity, emulating an artificial synapse that inherently possesses habituation and sensitization properties. Incorporating an additional resistive switching TiO layer into the HfO memristor exhibits third-order switching complexity and non-volatile habituation characteristics. Based on the third-order memristor, we propose a robotic system equipped with a memristor-based artificial sensory nervous system for optimizing the robot arm's response to external stimuli without the aid of processors. It is experimentally demonstrated that the robot arm with the developed memristor-based artificial sensory nervous system ignores approximately 71% of safe and familiar stimuli while sensitively responding to threatening and significant stimuli, similar to the habituation and sensitization of biological sensory nervous systems. Our findings can be a stepping stone for energy-efficient and intelligent robotic systems with reduced hardware burden.
动物的感觉神经系统能够感知外部刺激。为了实现能够有效响应外部刺激的神经启发式机器人，人们广泛开展了人工感觉神经系统的研发。然而，开发具有复杂生物学功能（如习惯化和敏感化）的人工感觉神经系统仍然具有挑战性，这些功能能够在没有庞大外围电路的情况下实现高效响应。在此，我们介绍一种具有三阶开关复杂性的忆阻器器件，它模拟了一种固有地具有习惯化和敏感化特性的人工突触。在氧化铪忆阻器中加入额外的电阻开关氧化钛层，表现出三阶开关复杂性和非易失性习惯化特性。基于三阶忆阻器，我们提出了一种配备基于忆阻器的人工感觉神经系统的机器人系统，用于在无需处理器辅助的情况下优化机器人手臂对外部刺激的响应。实验证明，配备了所开发的基于忆阻器的人工感觉神经系统的机器人手臂能够忽略大约71%的安全和熟悉的刺激，同时对威胁性和重大刺激敏感响应，这类似于生物感觉神经系统的习惯化和敏感化。我们的研究结果可为减轻硬件负担的节能和智能机器人系统奠定基础。
