JeongWoo Jeon

All Publications

• Neuromorphic olfaction with ultralow-power gas sensors and ovonic threshold switch SCIENCE ADVANCES Kang, M., Han, J., Lee, K., Jeong, J., Yoo, C., Jeon, J., Park, B., Choi, W., Ahn, J., Yoon, K., Hwang, C., Park, I. 2025; 11 (39): eadv9222

  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.

  View details for DOI 10.1126/sciadv.adv9222

  View details for Web of Science ID 001579007200018

  View details for PubMedID 40991689

  View details for PubMedCentralID PMC12459429

• Low-temperature atomic layer deposition of metastable MnTe films for phase change memory devices JOURNAL OF MATERIALS CHEMISTRY C Jeon, G., Jeon, J., Kim, W., Kim, D., Noh, W., Choi, W., Park, B., Jeon, S., Kim, S., Yoo, C., Hwang, C. 2025

  View details for DOI 10.1039/d4tc05499g

  View details for Web of Science ID 001431486600001