Abstract
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.
Original language | English |
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Pages (from-to) | 733-739 |
Number of pages | 7 |
Journal | Nano Letters |
Volume | 22 |
Issue number | 2 |
DOIs | |
State | Published - 26 Jan 2022 |
Bibliographical note
Publisher Copyright:© 2022 American Chemical Society
Keywords
- artificial retinal ganglion cell
- gate-tunable Ovonic threshold switch
- in-sensor computing
- neuromorphic
- spiking neural network