SINGAPORE, March 28, 2025 /PRNewswire/ — The team led by Associate Professor Mario Lanza from the Department of Materials Science and Engineering in the College of Design and Engineering at the National University of Singapore, has just revolutionised the field of neuromorphic computing by inventing a new super-efficient computing cell that can mimic the behaviour of both electronic neurons and synapses. The work, titled “Synaptic and neural behaviours in a standard silicon transistor” was published in the scientific journal Nature on 26 March 2025 and is already attracting interest from leading companies in the semiconductor field.
Electronic neurons and synapses are the two fundamental building blocks of next-generation artificial neural networks. Unlike traditional computers, these systems process and store data in the same place, eliminating the need to waste time and energy transferring data from memory to the processing unit (CPU). The problem is that implementing electronic neurons and synapses with traditional silicon transistors requires interconnecting multiple devices — specifically, at least 18 transistors per neuron and 6 per synapse. This makes them significantly larger and more expensive than a single transistor.
The team led by Professor Lanza has found an ingenious way to reproduce the electronic behaviours characteristic of neurons and synapses in a single conventional silicon transistor. The key lies in setting the resistance of the bulk terminal to a specific value to produce a physical phenomenon called “impact ionisation,” which generates a current spike very similar to what happens when an electronic neuron is activated. Additionally, by setting the bulk resistance to other specific values, the transistor can store charge in the gate oxide, causing the resistance of the transistor to persist over time, mimicking the behaviour of an electronic synapse. Making the transistor operate as a neuron or synapse is as simple as selecting the appropriate resistance for the bulk terminal. The physical phenomenon of “impact ionisation” had traditionally been considered a failure mechanism in silicon transistors, but Professor Lanza’s team has managed to control it and turn it into a highly valuable application for the industry.
This discovery is revolutionary because it allows the size of electronic neurons to be reduced by a factor of 18 and that of synapses by a factor of 6. Considering that each artificial neural network contains millions of electronic neurons and synapses, this could represent a huge leap forward in computing systems capable of processing much more information while consuming far less energy. Furthermore, the team has designed a cell with two transistors — called Neuro-Synaptic Random Access Memory (NSRAM) — that allows switching between operating modes (neuron or synapse), offering great versatility in manufacturing since both functions can be reproduced using a single block, without the need to dope the silicon to achieve specific substrate resistance values.
The transistors used by Professor Lanza’s team to implement these advanced neurons and synapses are not cutting-edge transistors like those manufactured in Taiwan or Korea, but rather traditional 180-nanometer node transistors, which can be produced by Singapore-based companies. According to Professor Lanza, “once the operating mechanism is discovered, it’s now more a matter of microelectronic design”.
The first author of the paper, Dr Sebastián Pazos, who is from King Abdullah University of Science and Technology, commented, “Traditionally, the race for supremacy in semiconductors and artificial intelligence has been a matter of brute force, seeing who could manufacture smaller transistors and bear the production costs that come with it. Our work proposes a radically different approach based on exploiting a computing paradigm using highly efficient electronic neurons and synapses. This discovery is a way to democratise nanoelectronics and enable everyone to contribute to the development of advanced computing systems, even without access to cutting-edge transistor fabrication processes.”
Read more at: https://news.nus.edu.sg/advancing-semiconductor-devices-for-artificial-intelligence.
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SOURCE National University of Singapore
