Artificial neurons enable neuromorphic computing with light
An adaptive capacitor that can sense and learn through optical stimuli enables learning and recognition tasks.
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Optoelectronic devices developed at KAUST that behave as either synapses or neurons, and adapt and reconfigure their response to light, could find use in optical neuromorphic information processing and edge computing.
The team from KAUST has designed and fabricated metal-oxide semiconductor capacitors (MOSCaps) based on the 2D material hafnium diselenide (HfSe2) that act as smart memories. The devices feature a vertical stack structure where HfSe2 is sandwiched between layers of aluminum oxide (Al2O3) and placed on a p-type silicon substrate. A transparent indium tin oxide (ITO) layer sits on top, allowing light to enter from above.
“When hafnium diselenide nanosheets are integrated into charge-trapping memory devices through solution-based processes, they enable both optical data sensing and retention capabilities,” says graduate student Bashayr Alqahtani. This allows the device to be reconfigured to sense light or store optical data after the light source is removed, depending on the bias conditions. “Our device is based on a two-terminal capacitive memory, which shows promise for device 3D stacking, paving the way for more adaptive and energy-efficient solutions,” she explains.
Read the full story on KAUST Discovery.
Reference
Alqahtani, B., Li, H., Syed, A.M. & El-Atab, N. From light sensing to adaptive learning: Hafnium dilselenide reconfigurable memcapacitive devices in neuromorphic computing. Light Science and Applications 14, 30 (2025).| article
Related People
Nazek El-Atab
- Assistant Professor, Electrical and Computer Engineering