Scandium Nitride Unveils Breakthrough in Brain-Like Computing | Fusion - WeRIndia

Scandium Nitride Unveils Breakthrough in Brain-Like Computing

Scandium Nitride Unveils Breakthrough in Brain-Like Computing

Unlocking the potential of brain-like computing, Bengaluru scientists have harnessed the power of scandium nitride (ScN).

It is a semiconducting wonder known for its stability and compatibility with Complementary Metal-Oxide-Semiconductor technology.

This innovation introduces a promising avenue for energy-efficient, optoelectronic synaptic functions, holding the promise of industrial implementation.

Unlike conventional computers with distinct memory and processing components, the brain stands as a model of efficiency.

And synapses perform both memory storage and processing roles. Recognizing the value of this biological design in the era of artificial intelligence, researchers are actively developing neuromorphic hardware to emulate synapses’ capabilities. Thus, it marks a significant leap in computing efficiency.

A dynamic team from Bengaluru’s Jawaharlal Nehru Centre for Advanced Scientific Research, leveraged their expertise in nitride-based materials to pioneer neuromorphic hardware.

Their groundbreaking work capitalizes on ScN to craft an artificial synapse, deftly controlling and retaining signal transmissions.

Led by Dheemahi Rao, the team engineered a ScN thin-film device that remarkably simulates diverse synaptic functionalities.

From short-term memory to transitioning between short and long-term memory, their innovation covers many things. Some of them are learning, forgetting, frequency-based filtering, logic-gate operations etc.

An outstanding attribute of their approach lies in the varying magnesium dopant concentrations, facilitating both excitatory and inhibitory operations within the same material, a rare feat.

By analyzing ScN’s response to light exposure, the team unlocked the door to excitatory (increased current) and inhibitory (decreased current) functions.

The lasting persistence of this photoconductivity underpins a memory mechanism, lasting from minutes to days depending on stimuli.

In a notable stride, this achievement marks the first instance of an optoelectronic synapse.

ScN outshines its peers in terms of stability and harmonious compatibility with existing silicon technology.

The processing techniques mirror semiconductor fabrication methods, highlighting their ease of implementation.

The team says that their innovation surpasses prior endeavours in many aspects.

In an era demanding efficient and adaptable computing solutions, ScN’s emergence as a versatile and energy-efficient option could well redefine the trajectory of artificial intelligence and computing technologies.

Image Credit: Maksym Kozlenko, CC BY-SA 4.0, via Wikimedia Commons

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