2D capacitors for in-memory AI image recognition in a light sensor

2D capacitors for in-memory AI image recognition in a light sensor

Technology News |
By Nick Flaherty

Researchers in Saudi Arabia have developed an image sensor that can perform in-memory AI scene recognition  using 2D capacitors.

The team at the King Abdullah University of Science and Technology (KAUST) adapted and enhanced the core structure of a charge coupled device (CCD) image sensor to create light-sensitive memory devices that can be programmed by light. In particular, the research team embedded the two-dimensional material MoS2 into a semiconductor capacitor (MOSCAP) structure that underpins the charge-storing pixels of a CCD sensor.

The Al/Al2O3/MoS2/Al2O3/Si MOSCAP structures function as a charge-trapping “in-memory” sensor that is sensitive to visible light and can be programmed optically and erased electrically.

“The in-memory light sensors are smart multifunctional memory devices that can perform the roles of multiple — traditionally discrete — devices at once, including optical sensing, storage and computation,” said Nazek El-Atab, Assistant Professor, Electrical and Computer Engineering at KAUST and Principal Investigator for the Smart, Advanced Memory devices and Applications lab.

“Our long-term goal is to be able to demonstrate in-memory sensors that can detect different stimuli and compute,” she said. “This overcomes the memory wall and allows for faster and more real-time data analysis using reduced power consumption, which is a requirement in many futuristic and state-of-the-art applications such as Internet of Things, autonomous cars and artificial intelligence, among others.”

Experiments with light with a wavelength anywhere in the blue to the red spectral region indicate that a photo-generated charge can be trapped or stored with an extremely long-lived retention time. The resulting “memory window” voltage of >2V can be stored for up to 10 years prior to being electrically erased by applying a +/-6V signal. It also operates for many millions of cycles.

The ultimate aim of the research is to create a single optoelectronic device that can perform optical sensing and storage with computing capabilities.

By combining the MoS2 MOSCAP structure with a neural network, the team showed that it was possible to perform simple binary image recognition, successfully distinguishing between images of either a dog or an automobile, with an accuracy of 91 percent. Each image was 32×32 pixels in size, and only the blue information from the images was extracted since that corresponds to the device’s peak sensitivity.

“Current memory devices can be programmed optically but require erasing electrically,” said researcher Dayanand Kumar. “In the future, we would like to explore in-memory optical sensors that can be fully optically operated.”

Memristor AI synapses

The team is also using black phosphorus to create an optoelectronic memristive synapse that mimics the brain’s neurons for neuromorphic computing applications.

The multilayer device consists of a thin layer of black phosphorus and hafnium oxide that is sandwiched between a lower layer of platinum and an upper layer of copper. It operates as an optoelectronic memristor — a resistor that can have its electrical resistance programmed by visible light.

Experiments indicate that it offers highly stable synaptic features, such as long-term potentiation (a long-lasting increase in signal output), long-term depression (a long-lasting decrease in signal output) and short-term plasticity (change in response over time).

The team constructed a 6×6 synaptic array from the devices, and in the future they hope that larger arrays could help realize a biomimetic retina. Importantly, the devices can be fabricated cost effectively by solution processing and are flexible with stable operation with a bend radius of 1 cm, offering possibilities for wearable applications.

DOI: 10.1038/s41377-023-01166-7


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