Inspired by information processing in biological systems, sensor-combined edge-computing systems attract attention requesting artificial sensory neurons as essential ingredients. Here, we introduce a simple and versatile structure of artificial sensory neurons based on a novel three-terminal Ovonic threshold switch (3T-OTS), which features an electrically controllable threshold voltage (Vth). Combined with a sensor driving an output voltage, this 3T-OTS generates spikes with a frequency depending on an external stimulus. As a proof of concept, we have built an artificial retinal ganglion cell (RGC) by combining a 3T-OTS and a photodiode. Furthermore, this artificial RGC is combined with the reservoir-computing technique to perform a classification of chest X-ray images for normal, viral pneumonia, and COVID-19 infections, releasing the recognition accuracy of about 86.5%. These results indicate that the 3T-OTS is highly promising for applications in neuromorphic sensory systems, providing a building block for energy-efficient in-sensor computing devices.
|Number of pages||7|
|Publication status||Published - 2022 Jan 26|
Bibliographical noteFunding Information:
This work was supported by the Korea Institute of Science and Technology (KIST) through 2E31040 and by the National Research Foundation program through NRF-2019M3F3A1A02072175, 2021M3F3A2A03017782, and 2021M3F3A2A01037814.
© 2022 American Chemical Society
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Condensed Matter Physics
- Mechanical Engineering