An Artificial Olfactory System Based on a Chemi-Memristive Device

Authors
Chun, Suk YeopSong, Young GeunKim, Ji EunKwon, Jae UkSoh, KeunhoKwon, Ju YoungKang, Chong-YunYoon, Jung Ho
Issue Date
2023-09
Publisher
WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Citation
Advanced Materials, v.35, no.35
Abstract
Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation, the redox reaction with the external gas molecules is enhanced, resulting in the generation and elimination of oxygen vacancies that induce rapid current changes. In addition, the pre-generated oxygen vacancies enhance the post-sensing properties. Therefore, fast responses, short recovery times, and hysteretic gas response are achieved by the proposed sensor at room temperature. Based on the advantageous functionality of the sensor, device-level olfactory systems that can monitor the history of input gas stimuli are experimentally demonstrated as a potential application. Moreover, analog conductance modulation induced by oxidizing and reducing gases enables the conversion of external gas stimuli into synaptic weights and hence the realization of typical synaptic functionalities without an additional device or circuit. The proposed chemi-memristive device represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.
Keywords
GAS SENSORS; NEURAL INTERFACES; IN-SITU; SENSITIVITY; MECHANISMS; NETWORK; artificial olfactory systems; gas sensors; hysteresis; memristors; olfactory synapses
ISSN
0935-9648
URI
https://pubs.kist.re.kr/handle/201004/113355
DOI
10.1002/adma.202302219
Appears in Collections:
KIST Article > 2023
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