ArticleScience advances2025
Neuromorphic olfaction with ultralow-power gas sensors and ovonic threshold switch.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Boosting Artificial Olfaction: Visual Cues-Enhanced Gas Classification by a Bimodal Neuromorphic Device.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Emerging Multimodal Artificial Sensory Fusion Paradigms Toward Advanced Embodied Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Sensitive and wafer-scale olfactory sensory neurons.Microsystems & nanoengineering · 2026Article
- Neuromorphic Near-Sensor and In-Sensor Computing Enabled by Next-Generation Material-Based Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Artificial Intelligence-Enabled Intelligent Sensory Systems for Quality Evaluation of Traditional Chinese Medicine: A Review of Electronic Nose, Electronic Tongue, and Machine Vision Approaches.Molecules (Basel, Switzerland) · 2026Review
- Bioinspired periodic mesoporous tin oxides enable steady nonequilibrium chemical sensing for enhanced gas discrimination.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With increasing demand for gas sensors in mobile devices, research on developing an electronic nose (E-nose) is actively conducted. However, conventional E-nose systems based on von Neumann computing have encountered challenges such as high hardware costs and power consumption because of the necessity of hardware-intensive circuits and processors. This work implements low-power artificial olfactory neuron modules within a spiking neural network (SNN) to address this issue. The artificial olfactory neuron module is developed by connecting a GeSe-based ovonic threshold switch and a micro-light-emitting diode (μLED) platform-based semiconductor metal oxide gas sensor in series. The use of μLED gas sensors enables ultralow-power operation, resulting in substantially decreased power consumption. The artificial olfactory neuron module generates spike signals with low operation voltage, demonstrating energy efficiency and advanced performance. A real-time gas classification based on the SNN is feasibly conducted with an accuracy of 99.6%. Moreover, it is possible to classify different ingredients under humidity disturbance conditions through a hardware SNN.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.