Photoactivated Organic Gas-Sensing Synapses with Enhanced Response and Memory for Neuromorphic Olfactory Systems

Abstract High-performance olfactory synapses are critical for efficient, wide-dynamic-range, and low-power neuromorphic sensing. Organic semiconductor devices, which offer advantages such as room-temperature operation, biocompatibility, and potential for large-scale manufacturing, represent a promising platform for olfactory synapse emulation. However, simultaneously enhancing both the synaptic weight and nonvolatility in organic semiconductor-based olfactory synapse devices remains a significant challenge. Here, we report a UV light activation strategy applied to a gas-sensing synaptic device based on the organic semiconductor dif-TES-ADT. Under UV illumination, the device exhibits a marked increase in response amplitude and a prolongation of recovery time. Exploiting this photoactivation effect, we successfully emulate both excitatory and inhibitory synaptic behaviors, achieving significantly enhanced synaptic weights and improved nonvolatility relative to those under dark conditions. Mechanistic studies suggest that this enhancement arises from the trapping of photogenerated charge carriers by gas molecules, which effectively increases the carrier density within the conduction channel. Furthermore, we demonstrate the device's capability to perform complex cognitive functions, including experience-dependent learning and Pavlovian conditioning. This work presents an effective strategy for synergistically regulating synaptic performance in organic neuromorphic devices, offering avenues for the development of multimodal perception and advanced neuromorphic computing systems.

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Publication Details

Journal
ACS Applied Electronic Materials
Published
2026-09-17
DOI
https://doi.org/10.1021/acsaelm.6c01529
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Photoactivated Organic Gas-Sensing Synapses with Enhanced Response and Memory for Neuromorphic Olfactory Systems

Lizhen Huang, Miao Xie, Lifeng Chi, Chenyu Zhu et al.
ACS Applied Electronic Materials
Advanced Memory and Neural Computing
article

Photoactivated Organic Gas-Sensing Synapses with Enhanced Response and Memory for Neuromorphic Olfactory Systems

Lizhen Huang, Miao Xie, Lifeng Chi, Chenyu Zhu, Cheng Shi, Yin Yao, Di Xue, Feng Ding
article en

Abstract

Abstract High-performance olfactory synapses are critical for efficient, wide-dynamic-range, and low-power neuromorphic sensing. Organic semiconductor devices, which offer advantages such as room-temperature operation, biocompatibility, and potential for large-scale manufacturing, represent a promising platform for olfactory synapse emulation. However, simultaneously enhancing both the synaptic weight and nonvolatility in organic semiconductor-based olfactory synapse devices remains a significant challenge. Here, we report a UV light activation strategy applied to a gas-sensing synaptic device based on the organic semiconductor dif-TES-ADT. Under UV illumination, the device exhibits a marked increase in response amplitude and a prolongation of recovery time. Exploiting this photoactivation effect, we successfully emulate both excitatory and inhibitory synaptic behaviors, achieving significantly enhanced synaptic weights and improved nonvolatility relative to those under dark conditions. Mechanistic studies suggest that this enhancement arises from the trapping of photogenerated charge carriers by gas molecules, which effectively increases the carrier density within the conduction channel. Furthermore, we demonstrate the device's capability to perform complex cognitive functions, including experience-dependent learning and Pavlovian conditioning. This work presents an effective strategy for synergistically regulating synaptic performance in organic neuromorphic devices, offering avenues for the development of multimodal perception and advanced neuromorphic computing systems.

ACS Applied Electronic Materials
Macau University of Science and Technology (MO), Soochow University (CN)
National Natural Science Foundation of China, Fundo para o Desenvolvimento das Ciências e da Tecnologia, Collaborative Innovation Center of Suzhou Nano Science and Technology
Openalex Percentile: Top 21%
Advanced Memory and Neural Computing
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