Ultrahigh Sensitive Human Inspired Neurons for Artificial Nociceptor Systems

Mimicking human brain functionalities with neuromorphic devices represents a pivotal breakthrough in developing bioinspired electronic systems. The human somatosensory system provides critical environmental information and facilitates responses to harmful stimuli, endowing us with good adaptive capabilities. However, current sensing technologies often struggle with insufficient sensitivity, dynamic response, and integration challenges. In this work, we present an ultrahigh sensitive cross-modal injury perception system based on a Pt/BaTiO<sub>3</sub> (BTO)/HfO<sub>2</sub>/TiN structure. This device can detect and process subtle changes in both tactile and visual stimuli. Its performance is primarily attributed to the incorporation of a BTO interlayer within the HfO<sub>2</sub> functional layer. The abundant oxygen vacancies (OVs) in the BTO layer enable precise control over the formation and disruption of conductive filaments (CFs) in the BTO/HfO<sub>2</sub> memristor, which results in a device exhibiting nociceptor-like characteristics with ultrahigh sensitivity. It demonstrates key features such as a threshold response, lack of adaptation, relaxation, and sensitization─where even a slight increase in stimulus triggers a significant response. Moreover, integrating the BTO/HfO<sub>2</sub> sensor into a cross-modal system enables the simulation of visual, tactile, and pain perception, highlighting its potential for advanced human-machine interaction applications.

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Publication Details

Journal
ACS Nano
Published
2025-09-09
DOI
https://doi.org/10.1021/acsnano.5c09330
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.19
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article

Ultrahigh Sensitive Human Inspired Neurons for Artificial Nociceptor Systems

Jeongmin Hong, Leon O. Chua, Lu Liu, Cong Ye et al.
3 citations
ACS Nano
Advanced Memory and Neural Computing
2.19
article

Ultrahigh Sensitive Human Inspired Neurons for Artificial Nociceptor Systems

Jeongmin Hong, Leon O. Chua, Lu Liu, Cong Ye, Xingjuan Song, Rui Su, Yi Sun, Bin Ren, Jinxia Xu, W. L. Xu, Jingping Xu
article en
3 citations

Abstract

Mimicking human brain functionalities with neuromorphic devices represents a pivotal breakthrough in developing bioinspired electronic systems. The human somatosensory system provides critical environmental information and facilitates responses to harmful stimuli, endowing us with good adaptive capabilities. However, current sensing technologies often struggle with insufficient sensitivity, dynamic response, and integration challenges. In this work, we present an ultrahigh sensitive cross-modal injury perception system based on a Pt/BaTiO<sub>3</sub> (BTO)/HfO<sub>2</sub>/TiN structure. This device can detect and process subtle changes in both tactile and visual stimuli. Its performance is primarily attributed to the incorporation of a BTO interlayer within the HfO<sub>2</sub> functional layer. The abundant oxygen vacancies (OVs) in the BTO layer enable precise control over the formation and disruption of conductive filaments (CFs) in the BTO/HfO<sub>2</sub> memristor, which results in a device exhibiting nociceptor-like characteristics with ultrahigh sensitivity. It demonstrates key features such as a threshold response, lack of adaptation, relaxation, and sensitization─where even a slight increase in stimulus triggers a significant response. Moreover, integrating the BTO/HfO<sub>2</sub> sensor into a cross-modal system enables the simulation of visual, tactile, and pain perception, highlighting its potential for advanced human-machine interaction applications.

ACS NanoVol. 19(37)
Ministry of Education of the People's Republic of China (CN), Dongguan University of Technology (CN), University of California System (US), Huazhong University of Science and Technology (CN), Amec Foster Wheeler (United States) (US), Hubei University of Technology (CN), Hubei University (CN), University of California, Berkeley (US)
Openalex Percentile: Top 11%
Advanced Memory and Neural Computing
2.19
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