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Three-Terminal Ovonic Threshold Switch (3T-OTS) with Tunable Threshold Voltage for Versatile Artificial Sensory
Hyejin Lee1,2, Seong Won Cho1,3, Seon Jeong Kim1,4
1Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Korea.
Nano Letters
|January 13, 2022
Summary
Researchers developed artificial sensory neurons using a novel three-terminal Ovonic threshold switch (3T-OTS). This technology enables efficient in-sensor computing for neuromorphic systems, demonstrated by classifying COVID-19 chest X-rays with 86.5% accuracy.
Area of Science:
- Neuromorphic Engineering
- Materials Science
- Biomedical Imaging
Background:
- Biological systems inspire sensor-combined edge-computing, requiring artificial sensory neurons.
- Existing systems lack efficient, versatile artificial neuron structures.
Purpose of the Study:
- To introduce a novel three-terminal Ovonic threshold switch (3T-OTS) for artificial sensory neurons.
- To demonstrate the 3T-OTS's capability in creating functional neuromorphic systems.
Main Methods:
- Developed a three-terminal Ovonic threshold switch (3T-OTS) with controllable threshold voltage.
- Constructed an artificial retinal ganglion cell (RGC) using a 3T-OTS and photodiode.
- Integrated the artificial RGC with reservoir computing for image classification.
Main Results:
- The 3T-OTS generates stimulus-dependent spikes, mimicking biological neurons.
- The artificial RGC achieved 86.5% accuracy in classifying chest X-ray images (normal, viral pneumonia, COVID-19).
Conclusions:
- The 3T-OTS is a promising building block for energy-efficient, in-sensor computing in neuromorphic sensory systems.
- This approach offers a pathway towards advanced, bio-inspired edge computing devices.
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