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Optoelectronic Superstructure Could Be Applied in Neuromorphic Computing

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ORLANDO, Fla., Feb. 20, 2020 — University of Central Florida (UCF) researchers have taken a step toward developing neuromorphic computers — computers that mimic neurobiological architectures present in the human nervous system — by demonstrating how to create a nanoscale device that can mimic the neural pathways of brain cells used for human vision. “Our group created a single device that mimics the eye and the brain function together,” professor Tania Roy said. “Our device can observe the image and recognize it on the spot.”

The researchers grew perovskite quantum dots (PQDs) directly on 2D, atomic-thick nanomaterial (graphene). They used the graphene-PQD superstructure to develop ultrathin phototransistors and photonic synapses.

The graphene-PQD superstructure synchronized efficient charge generation and transport on the single platform. The PQDs captured light, converted the light to electric charges, and transferred the charges directly to the graphene, all in one step. The entire process took place on an extremely thin film, about one ten-thousandths of the thickness of a human hair. The graphene-PQD phototransistors exhibited excellent responsivity, the team said. Moreover, the light-assisted memory effect of these superstructures enabled photonic synaptic behavior.

To test their device’s ability to see objects through neuromorphic computing, the researchers used it in facial recognition experiments assisted by machine learning. “The facial recognition experiment was a preliminary test to check our optoelectronic neuromorphic computing,” professor Jayan Thomas said. “Since our device mimics vision-related brain cells, facial recognition is one of the most important tests for our neuromorphic building block.” 



They found that their device was able to successfully recognize the portraits of four different people. “Because of the nature of the superstructure, it shows a light-assisted memory effect,” Basudev Pradhan, assistant professor at the Central University of Jharkhand in India, said. “This is similar to humans’ vision-related brain cells. The optoelectronic synapses we developed are highly relevant for brain-inspired, neuromorphic computing.”

The researchers hope that the graphene-PQD superstructures will open new directions in the development of highly efficient optoelectronic devices. Potential applications include drone-assisted rescues and defense. “Such features can also be used for aiding the vision of soldiers on the battlefield,” researcher Sonali Das said. “Further, our device can sense, detect, and reconstruct an image along with extremely low power consumption, which makes it capable for long-term deployment in field applications.” The team plans to continue its collaboration to refine the device, including using it to develop a circuit-level system.

The research was published in Science Advances (www.doi.org/10.1126/sciadv.aay5225). 


Published: February 2020
Glossary
quantum dots
A quantum dot is a nanoscale semiconductor structure, typically composed of materials like cadmium selenide or indium arsenide, that exhibits unique quantum mechanical properties. These properties arise from the confinement of electrons within the dot, leading to discrete energy levels, or "quantization" of energy, similar to the behavior of individual atoms or molecules. Quantum dots have a size on the order of a few nanometers and can emit or absorb photons (light) with precise wavelengths,...
perovskite
The term perovskite refers to a specific crystal structure commonly found in various materials. Perovskite structures have a cubic arrangement of oxygen ions, forming a framework within which other cations (positively charged ions) are located. This crystal structure was named after the mineral perovskite, which has the chemical formula CaTiO3 and was first discovered in the Ural Mountains of Russia. The general formula for the perovskite structure is ABX3, where: A represents a larger...
graphene
Graphene is a two-dimensional allotrope of carbon consisting of a single layer of carbon atoms arranged in a hexagonal lattice pattern. It is the basic building block of other carbon-based materials such as graphite, carbon nanotubes, and fullerenes (e.g., buckyballs). Graphene has garnered significant attention due to its remarkable properties, making it one of the most studied materials in the field of nanotechnology. Key properties of graphene include: Two-dimensional structure: Graphene...
optoelectronics
Optoelectronics is a branch of electronics that focuses on the study and application of devices and systems that use light and its interactions with different materials. The term "optoelectronics" is a combination of "optics" and "electronics," reflecting the interdisciplinary nature of this field. Optoelectronic devices convert electrical signals into optical signals or vice versa, making them crucial in various technologies. Some key components and applications of optoelectronics include: ...
machine learning
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to improve their performance on a specific task through experience or training. Instead of being explicitly programmed to perform a task, a machine learning system learns from data and examples. The primary goal of machine learning is to develop models that can generalize patterns from data and make predictions or decisions without being...
nano
An SI prefix meaning one billionth (10-9). Nano can also be used to indicate the study of atoms, molecules and other structures and particles on the nanometer scale. Nano-optics (also referred to as nanophotonics), for example, is the study of how light and light-matter interactions behave on the nanometer scale. See nanophotonics.
Research & TechnologyeducationAmericasUniversity of Central Floridaneuromorphic computingImagingquantum dotsperovskitegrapheneMaterialsphototransistorsoptoelectronicsfacial recognitionOpticsdefensemachine learningphotonic synapsenano

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