From lab to life – atomic-scale memristors pave the way for brain-like AI and next-gen computing power
- Memristors to bring brain-like computing to AI systems
- Atomically tunable devices offer energy-efficient AI processing
- Neuromorphic circuits open new possibilities for artificial intelligence
A new frontier in semiconductor technology could be closer than ever after the development of atomically tunable “memristors” which are cutting-edge memory resistors that emulate the human brain’s neural network.
With funding from the National Science Foundation’s Future of Semiconductors program (FuSe2), this initiative aims to create devices that enable neuromorphic computing – a next-generation approach designed for high-speed, energy-efficient processing that mimics the brain’s ability to learn and adapt.
At the core of this innovation is the creation of ultrathin memory devices with atomic-scale control, potentially revolutionizing AI by allowing memristors to act as artificial synapses and neurons. These devices have the potential to significantly enhance computing power and efficiency, opening new possibilities for artificial intelligence applications, all while training a new generation of experts in semiconductor technology.
Neuromorphic computing challenges
The project focuses on solving one of the most fundamental challenges in modern computing: achieving the precision and scalability needed to bring brain-inspired AI systems to life.
To develop energy-efficient, high-speed networks that function like the human brain, memristors are the key components. They can store and process information simultaneously, making them particularly suited to neuromorphic circuits where they can facilitate the type of parallel data processing seen in biological brains, potentially overcoming limitations in traditional computing architectures.
The joint research effort between the University of Kansas (KU) and the University of Houston led by Judy Wu, a distinguished Professor of Physics and Astronomy at KU is supported by a $1.8 million grant from FuSe2.
Wu and her team have pioneered a method for achieving sub-2-nanometer thickness in memory devices, with film layers approaching an astonishing 0.1 nanometers — approximately 10 times thinner than the average nanometer scale.
These advancements are crucial for future semiconductor electronics, as they allow for the creation of devices that are both extremely thin and capable of precise functionality, with large-area uniformity. The research team will also use a co-design approach that integrates material design, fabrication, and testing.
In addition to its scientific aims, the project also has a strong focus on workforce development. Recognizing the growing need for skilled professionals in the semiconductor industry, the team has designed an educational outreach component led by experts from both universities.
“The overarching goal of our work is to develop atomically ‘tunable’ memristors that can act as neurons and synapses on a neuromorphic circuit. By developing this circuit, we aim to enable neuromorphic computing. This is the primary focus of our research,” said Wu.
“We want to mimic how our brain thinks, computes, makes decisions and recognizes patterns — essentially, everything the brain does with high speed and high energy efficiency.”
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Memristors to bring brain-like computing to AI systems Atomically tunable devices offer energy-efficient AI processing Neuromorphic circuits open new possibilities for artificial intelligence A new frontier in semiconductor technology could be closer than ever after the development of atomically tunable “memristors” which are cutting-edge memory resistors that emulate the human…
