Neuromorphic olfaction with ultralow-power gas sensors and ovonic threshold switch.

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

SUBMITTER: Kang M 

PROVIDER: S-EPMC12459429 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature