AI Article Synopsis
- Nanomaterials like Tellurium (Te) combined with sulfur and selenium are being studied for their potential in creating low-power optical synapses that mimic biological neural functions.
- The research highlights the enhanced properties of Tellurium sulfur oxide (TeSO) and Tellurium selenium oxide (TeSeO) compared to pure Te, particularly their ability to respond to specific light wavelengths.
- The TeSO and TeSeO devices show promise for optical neuromorphic computing due to their high responsiveness to UV and visible light, efficient energy use, and potential for reliable performance in synaptic applications.
Article Abstract
Nanomaterials like graphene and transition metal dichalcogenides are being explored for developing artificial photosensory synapses with low-power optical plasticity and high retention time for practical nervous system implementation. However, few studies are conducted on Tellurium (Te)-based nanomaterials due to their direct and small bandgaps. This paper reports the superior photo-synaptic properties of covalently bonded Tellurium sulfur oxide (TeSO) and Tellurium selenium oxide (TeSeO)nanomaterials, which are fabricated by incorporating S and Se atoms on the surface of Te multiropes using vapor deposition. Unlike pure Te multiropes, the TeSO and TeSeO multiropes exhibit controllable temporal dynamics under optical stimulation. For example, the TeSO multirope-based transistor displays a photosensory synaptic response to UV light (λ = 365 nm). Furthermore, the TeSeO multirope-based transistor exhibits photosensory synaptic responses to UV-vis light (λ = 365, 565, and 660 nm), reliable electrical performance, and a combination of both photodetector and optical artificial synaptic properties with a maximum responsivity of 1500 AW to 365 nm UV light. This result is among the highest reported for Te-heterostructure-based devices, enabling optical artificial synaptic applications with low voltage spikes (1 V) and low light intensity (21 µW cm), potentially useful for optical neuromorphic computing.
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| http://dx.doi.org/10.1002/smll.202310013 | DOI Listing |
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