2026-07-02 00:01:27
This article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia.
As artificial intelligence accelerates global demand for compute, a parallel constraint is emerging with equal urgency: energy.
From hyperscale data centers to electrified industries, AI is driving a step change in electricity demand. This is not a future challenge, it is a present, system-level issue requiring coordinated action across energy, infrastructure, and engineering disciplines.
Around the world, the question is no longer whether AI will scale, but whether energy systems can scale with it.
Melbourne, Australia is moving beyond participation to become a globally connected leader helping define how these challenges are addressed.
Australia’s ambition to lead in artificial intelligence is sharpening focus on the infrastructure required to support it. Data centers are projected to account for up to 11 percent of the nation’s electricity consumption by 2035, placing increasing pressure on generation, transmission, and system reliability.
At the same time, insight from the IEEE Power and Energy Society (PES) highlights that meeting energy demand from AI and digital infrastructure is one of the most significant challenges facing engineers over the next decade.
The implications are clear. In addition to computing challenges, AI poses major energy systems challenges.
“As artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support it” —Professor Thas (Ampalavanapillai) Nirmalathas, University of Melbourne
Victoria has developed one of the most advanced and integrated energy ecosystems in Australia and globally, spanning renewable generation, battery storage, grid modernization, and advanced materials.
What distinguishes Melbourne globally is how these capabilities are connected and applied at system scale.
The city brings together world class engineering research, a rapidly evolving clean energy sector, advanced digital infrastructure, and strong alignment between government, industry, and academia. This convergence is critical in the AI era, where energy, networks and computing systems must be designed together.
Victoria’s coordinated investment across these areas is positioning Melbourne not only as a national leader, but also as a reference point in the global energy system transformation.
The challenge ahead is that generating more power won’t be enough, as engineers need to design systems that respond dynamically to new patterns of demand.
Three priorities are emerging globally:
Addressing these priorities requires engineering expertise to be embedded earlier in planning ensuring energy systems, digital infrastructure, and policy are designed in parallel.
Melbourne’s strength lies in its ability to integrate this expertise across research, infrastructure, and real-world application.
Melbourne Connect is a University of Melbourne–led innovation precinct, supported by government and industry, designed to bring together research, business and policy to deliver real-world solutions.Atlantic Group
At the centre of this capability is the University of Melbourne, where interdisciplinary research is advancing the systems required to support AI driven energy demand.
Through the Melbourne Energy Institute, for example, researchers are examining how energy technologies interact across entire systems from generation and networks through to end use.
“As artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support it,” says Professor Thas (Ampalavanapillai) Nirmalathas, Dean of the Faculty of Engineering and Information Technology at the University of Melbourne.
“This is driving a new level of convergence between digital infrastructure and power systems engineering, where integrated, system level thinking is essential.”
Melbourne’s leadership is further strengthened by world-class interdisciplinary facilities such as the Smart Grid Lab in the Department of Electrical and Electronic Engineering, which enables real-time simulation of power systems, allowing engineers to test how solar, batteries, electric vehicles and other distributed resources interact within future grids. This supports the design of more resilient, efficient energy systems before they are deployed at scale.
Melbourne’s Smart Grid Lab in the Department of Electrical and Electronic Engineering enables real-time simulation of power systems. University of Melbourne
These capabilities will become increasingly important as data centers are integrated into the grid.
“AI driven demand is not only increasing computing requirements, but also placing new pressures on underlying energy systems,” says Glen Farivar, Senior Lecturer in Power Electronics at the University of Melbourne. “Designing these systems together is essential to achieving both performance and sustainability outcomes.”
This reflects a critical shift. Future infrastructure must be co designed across energy and digital systems, not developed in isolation.
Victoria’s broader energy ecosystem is translating these insights into practice.
Investment in renewable energy, grid infrastructure and storage is enabling higher levels of clean energy while maintaining reliability. Battery deployment is supporting the flexibility needed to manage both renewable variability and growing AI-driven demand.
At its core, Melbourne offers an integrated environment where research, industry and government collaborate to solve complex system challenges.
Solving the energy demands of the AI era cannot be achieved in isolation.
It requires engineers, researchers, utilities, and policymakers to work together earlier and more often. More than ever, engineering collaboration is a critical enabler of future energy systems.
Environments that bring together global expertise are becoming essential to how solutions are designed and delivered.
“Developing future energy systems that are affordable, sustainable, and resilient is a truly grand challenge” —Professor Pierluigi Mancarella, University of Melbourne
In this context, the University of Melbourne is co-leading, alongside Johns Hopkins University and Imperial College London, one of only seven Global Centres in Climate Change and Clean Energy. Through the Electric Power Innovation for a Carbon Free Society (EPICS) Centre, the University is also the Australian technical lead in advancing future energy systems, with EPICS the only Global Centre focused on future energy infrastructure.
The new Electric Power Innovation for a Carbon-Free Society (EPICS) Centre will address challenges in clean energy production and storage.University of Melbourne
“Developing future energy systems that are affordable, sustainable, and resilient is a truly grand challenge,” says Professor Pierluigi Mancarella, Chair Professor of Electrical Power Systems at the University of Melbourne and Australian director and international co-director of EPICS.
“As electricity grids are increasingly becoming the backbone of future energy systems, optimizing their interactions with other sectors, including AI and digitalization, and fostering interdisciplinary and international collaborations are essential,” he adds.
International conferences are increasingly recognized as critical platforms for advancing engineering solutions at scale. Melbourne’s ability to convene global expertise is central to its leadership.
In 2027, the city will host the IEEE PES Generation Transmission and Distribution (GTD) Asia 2027 Conference and Exposition, bringing together engineers, utilities, researchers and policymakers from across the world to address the challenges shaping the future of power systems.
IEEE PES GTD Asia 2027 Melbourne Committee (left to right): Dr. Mehdi Ghazavi Dozein (Monash University), Dr. Glen Farivar & Professor Pierluigi Mancarella (University of Melbourne) , Dr. Mohammad Mohammadi (Australian Energy Market Operator (AEMO)).MCB
“Melbourne offers a unique environment where world-class research, industry capability and policy leadership come together,” notes the IEEE PES GTD Asia 2027 Local Organising Committee, which includes Professor Pierluigi Mancarella and Dr. Glen Farivar from the University of Melbourne, as well as Dr. Mehdi Ghazavi Dozein of Monash University and Dr. Mohammad Mohammadi of the Australian Energy Market Operator.
“Hosting this event creates an opportunity to advance global collaboration on the systems and technologies required to deliver the energy transition at scale.”
These forums enable knowledge exchange, standards development and interdisciplinary collaboration, accelerating progress on complex engineering challenges.
Attendees view a digital installation at AIME 2025 at Melbourne Connect.MCB
As AI, electrification and digital infrastructure converge, the future of global energy systems will depend on the ability of engineers to collaborate and innovate at scale.
Melbourne provides a proven platform for that collaboration, combining world-class research, a rapidly evolving energy ecosystem, and the infrastructure to connect global expertise.
Melbourne Convention Bureau, IEEE Communications Society, and University of Melbourne Representatives.University of Melbourne
For IEEE members, hosting a conference in Melbourne is more than an event decision.
It is an opportunity to engage with a globally connected engineering community and contribute directly to solving one of the most significant challenges facing the profession today.
Through the support of the Melbourne Convention Bureau, professionals can access tailored, free support to bid for and deliver international conferences, bringing global expertise together in a city actively shaping the future of energy systems.
To explore hosting your next conference in Melbourne, contact the Melbourne Convention Bureau at [email protected].
2026-07-01 20:00:01
“The lowest-cost place to put AI will be in space, and that will be true within two years, maybe three at the latest,” SpaceX founder Elon Musk told the World Economic Forum in Davos this past January, as his company was preparing to go public.
Later that month, SpaceX filed an application with the Federal Communications Commission for an orbital data center constellation of up to 1 million satellites in low Earth orbit, 500 to 2,000 kilometers above Earth. And just three days before the IPO, he discussed some initial design specifications for a new AI-1 satellite data center in a video interview.
Musk is prone to hyperbole when it comes to timelines. Full self-driving cars by 2017. First human mission to Mars in 2024. Ten thousand Optimus humanoid robots by the end of 2025. Et cetera. For orbital data centers, which he says will be a cost-effective alternative to terrestrial data centers within three years, the math won’t make sense for several years, if ever.
Consider this: There are roughly 14,500 active satellites in orbit. Musk’s Starlink constellation accounts for about two thirds of those. Both the launch cadences and satellite-manufacturing capacity would have to scale up astronomically to deploy a million orbital data center satellites.
For context, there have been roughly 7,000 orbital launches in all of human history. To loft 1 million satellites into low Earth orbit on SpaceX’s Starship, which is designed to carry up to 60 satellites per vehicle, would require 16,666 launches exclusively devoted to satellite deployments. Considering that SpaceX launched a record 165 orbital missions in 2025, even at 10 times that cadence, it would take a decade. And how long would it take to build 1 million satellites, given Starlink’s current pace of around 4,000 per year and a generous tenfold increase in capacity? Short of a manufacturing revolution, try 25 years.
The reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized.
As this month’s cover story, “Why Orbital Data Centers Are So Hard” by Andrew Cavalier of ABI Research, makes clear, the reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized.
Dina Genkina, IEEE Spectrum’s computing and hardware editor, put the idea into perspective: “Starcloud (a startup that has applied to the FCC for an 88,000 orbital data center satellite constellation) sent one Nvidia H100 GPU in space so far. Their radiator was too weak to let the chip run at full power.”
As Cavalier shows, cooling even a single Nvidia H100 GPU in space is difficult: It draws 700 watts, which will require 1.4 square meters of radiator at 60 °C. A 40-kilowatt rack of servers will need an 80-m² radiator; a 100-megawatt data center will require 2,500 of those radiators. Some astronomers are understandably concerned that a million satellites with giant radiative wings would blot out the stars.
So if the economics doesn’t make sense, if the chips are at the mercy of the radiative ravages of space, and if humanity will lose its view of the stars, not to mention increasing the risk of triggering the Kessler syndrome, why are the hyperscalers hyping orbital data centers?
Genkina offered the obvious answer: sweet, sweet moolah. “The Elon Musk part of it is honestly genius because he’s got xAI building the data centers, SpaceX sending them to space, and Tesla building solar panels,” Genkina says. “It’s almost like he’s paying himself.”
Michael Pierce, Principal at Technology Strategy Partners
Musk’s timelines are notoriously overly ambitious, but I think SpaceX’s orbital data centers might reach cost parity with terrestrial data centers in 5 to 10 years. The Starlink laser-link network already exists as the communication backbone for any SpaceX compute constellation, and that infrastructure is what no new entrant can replicate quickly. The chip-agnostic payload design probably reflects their disclosed difficulty securing AI silicon as much as any modularity philosophy. My view is that the only realistic near-term application is a SpaceX mega-constellation for inference. Training workloads likely cannot tolerate the synchronization and latency constraints of a distributed orbital system.
Our report analyzed the market from the integrator’s vantage point, but AI1 is what it looks like when one player has assembled all the necessary advantages simultaneously. The question is whether the terrestrial data center industrial base will degrade or improve on economics. I don’t have insight into SpaceX’s internal costs, as opposed to public pricing, on all their components, so it’s hard to say if they’ll completely dominate or not. Even if they are not cost competitive with terrestrial data centers for another 5 to 10 years, it may simply be faster to get new compute that just happens to be in space.
Matt Hasan, AI strategist and independent consultant
My initial view is that AI1 does not fundamentally change the rationale for space-based data centers as much as it changes the timeline and scale. The underlying drivers remain the same: escalating AI compute demand, growing power constraints on terrestrial grids, and the desire to colocate energy generation with computation.
What AI1 does signal is that the concept is beginning to move from theoretical discussion toward engineering and capital allocation decisions. The announcement adds credibility to the idea that hyperscale computing infrastructure may eventually expand beyond terrestrial constraints rather than simply competing for increasingly scarce grid capacity on Earth.
That said, significant economic and technical questions remain. Launch costs, maintenance, hardware replacement cycles, thermal management, latency-sensitive workloads, and overall system economics will ultimately determine whether space-based data centers become a mainstream extension of AI infrastructure or remain a niche capability for specialized applications. The key development is not that these questions have been resolved, but that major industry players now appear willing to invest resources toward answering them.
2026-06-30 21:00:02
In the 1970s, American Fireworks, a family-run pyrotechnics company in Hudson, Ohio, used a “home run box” to offer quick and easy fireworks displays for the Cleveland Indians (now the Cleveland Guardians) baseball games.
The red wooden crate had metal silos to store the rockets. Each switch on the control panel allowed the operator to set off a different firing sequence. This setup instantly triggered the display whenever a Cleveland batter hit a home run. Before computerized firing systems became common, panels like this represented the state of the art. But they did not eliminate human error. On 15 September 2015, the technician in charge of the Indians’ pyrotechnics accidentally set off the fireworks when the opposing team hit a home run. The embarrassed technician was caught on camera holding his head in his hands.
This home run box and control panel [left] were used to launch fireworks during Cleveland Indians games. The rockets were housed in metal silos within the box.Left: Jahna Auerbach/Science History Institute; Right: American Fireworks
Fireworks are one of the many Song Dynasty inventions that migrated from China through the Middle East and into Europe by way of trade routes. Around 200 B.C.E, the Chinese invented small firecrackers by simply tossing pieces of bamboo into a fire. The air inside the bamboo would expand and crack the wood, and the pop supposedly scared away evil spirits. After the invention of gunpowder—a mixture of sulfur, charcoal, and potassium nitrate—about a thousand years later, some clever person thought to pack the powder into the bamboo tubes and ignite them, launching the first fireworks—and the first rockets—into the sky.
John Bate’s popular 1634 book on fireworks described fire wheels [left] and a flying dragon [right], consisting of a dragon-shaped rocket that sped along a rope. SSPL/Getty Images
By the Renaissance, specialized schools for pyrotechnics had emerged across Italian city-states, and European craftsmen began creating large spectacles for royal occasions and religious celebrations. In 1634, John Bate published the four-volume series The Mysteries of Nature and Art, the second of which described how to create all manner of fireworks. Woodcut illustrations showed fire wheels (now called pinwheels or Catherine wheels), as well as the more ambitious flying dragon—a rocket shaped like a dragon that emitted sparks while speeding across a rope strung between two buildings.
During the 18th and 19th centuries, chemists and alchemists discovered new chemical compounds and isolated new elements that expanded the palette for fireworks. Adding barium nitrate produced green, for example, and strontium nitrate produced red. Chemists also mixed in metal particles to create sparkles.
The 1880s saw the introduction of the loud screech or whistle that precedes the exploding boom. Amédée Denisse, a graphic artist by trade and a fireworks hobbyist, discovered that a cardboard tube containing potassium picrate added that satisfying auditory effect to his fireworks display.
British colonists brought fireworks to the Americas. In 1608, Captain John Smith set them off to celebrate the founding of Jamestown, Virginia, the first permanent English settlement in what would become the United States. More than a century and a half later, while the Continental Congress was meeting in Philadelphia in July 1776, future U.S. president John Adams speculated in a letter to his wife that Independence Day would be celebrated “with pomp and parade, with shews, games, sports, guns, bells, bonfires and illuminations from one end of this continent to the other.”
Although Adams got the day wrong—he mistakenly thought the committee would complete the revisions to the Declaration of Independence by the 2nd of July—he was correct in foreseeing that Independence Day would be celebrated with lots and lots of fireworks. Just a year later, on 5 July 1777, the Pennsylvania Evening Post reported on the grand exhibition of fireworks the previous night, which began and concluded with 13 rockets representing the 13 colonies.
It’s safe to say that the United States is still obsessed with fireworks. According to the American Pyrotechnics Association, the country spends about US $3 billion on fireworks each year; it’s also the leading importer of fireworks. As the U.S. gears up to celebrate its 250th birthday this 4th of July, expect to see fireworks displays everywhere, from kids with sparklers running in backyards to ambitious professional displays for huge crowds.
Modern fireworks displays like the Macy’s 4th of July celebration in New York City are computer choreographed and controlled. Roy Rochlin/Getty Images
Fireworks today are an engineering marvel. State-of-the-art displays are computer controlled with precise digital timing, often tied to musical accompaniment. Designers can spend weeks choreographing complicated patterns and assigning launch times, shell types, and colors. The completed script is uploaded to an electronic firing system, which consists of the control panel and hundreds or thousands of firing modules that connect to the rockets. It can take days to set up the launch site for a large-scale display that lasts just minutes.
For example, last year more than 60 licensed pyrotechnicians worked for 12 days to arrange more than 80,000 shells for the Macy’s 4th of July Fireworks in New York City. Each of the firework shells measured up to 25 centimeters in diameter and weighed more than 13 kilograms—a far cry from their bamboo ancestors. More than 120 kilometers of wire connected the bundles of explosives to twelve computers. All that for a 25-minute display.
As much as I unabashedly love fireworks, they’re not for everyone and they do have a downside. The explosions can trigger PTSD for military veterans, and they can also upset animals. Every year, thousands of people are injured by mishandled or damaged fireworks. Known to set off wildfires, fireworks are often banned during droughts. Scientists who’ve studied the environmental impact of fireworks displays have noted their tendency to disperse airborne metallic particles and other harmful particulates.
A drone light show over Busan, South Korea, shows a member of the K-pop band BTS.Hwawon Ceci Lee/Anadolu/Getty Images
Perhaps to counter those drawbacks, or maybe it’s just the next technological evolution in aerial display, companies are now offering drone light shows. Fleets of hundreds or thousands of LED-toting drones can be programmed to hover in the air and fly in formation, forming logos and other designs that are more stable than exploding fireworks.
These exquisitely choreographed light shows are truly impressive. And yet I relish the full sensory experience of fireworks, including the booms, the smoke, and the smell. So whether you’re celebrating your country’s birth, Guy Fawkes Day, Saint Sylvester’s Night, New Year’s, Diwali, or simply cheering a home run from your favorite team, I hope you get to enjoy this millennia-old technological marvel.
Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.
An abridged version of this article appears in the July 2026 print issue as “Rooting for the Home Team.”
The American Pyrotechnics Association is a professional organization that encourages safety in design and use of all types of fireworks, provides industry support, and promotes responsible regulation.
Barry Sturman and David Garrioch’s 2023 article “Amateur Science and Innovation in Fireworks in Nineteenth-Century Europe,” in the journal Ambix, provides a detailed history of the development of fireworks. Kathy De Antonis’s 2010 article “Fireworks!” for a publication of the American Chemical Society explains the colors, shapes, and packaging of modern fireworks.
If you happen to find yourself in Philadelphia before the end of July, check out the Science History Institute’s exhibit Flash! Bang! Boom! A History of Fireworks, which is part of the U.S. celebrations around the semiquincentennial. The home run box shown in this article is part of the institute’s collections.
2026-06-30 20:24:33
He was born into a storm, lightning split the summer sky, in a
village the world had not yet heard of.
The midwife called it a bad omen, his mother called it a sign. Your first
life began in a storm, under open sky.
One winter night you ran your hand along a cat’s back, and the
darkness cracked open with sparks.
Your mother warned the house could burn.
You were already chasing what you learned: Light would return.
Your second life came underwater, in the current deep. No light,
no air, the river pulling you under,
the surface closing above you without a sound, and
something in you refused to sink or sleep.
Your third life came at the dam.
The water rose. The wall held you in place.
One flash, you turned your body and rose back into air, and left
the weight of water without a trace.
Your fourth life came in stone and dark. Entombed for a
night in a mountain chapel,
visited by no one. Only silence and the memory of a spark. You called
it an awful experience and left it there, untold.
Your fifth life came in fever,
nine months cholera held you down,
until your father said: Survive, and choose your own ground. You rose.
Not from the prayer, but from the promise he made.
Your sixth life came in silence, and it stayed.
Every sound cut through you, a clock three rooms away,
a ringing that would not leave, a noise you learned to bear, until you
lived inside that noise and made a home in there.
Your seventh life burned on Fifth Avenue, not your body, but your work. Not a thief
of fire, but one who stayed with the blaze.
A modern Prometheus, your life’s work turned to ash,
“I must begin again,” you said, and turned to new ways.
Your eighth life came in the street.
No storm. No warning. A taxi struck without a sign. A
sudden impact: ribs breaking, breath gone.
No diagram this time. Only the body, slow to keep up.
The ninth life came on quiet wings.
That dove found you in the dark, and your spirit rose. She did
not move. A beam of light fell from above.
The life you would not return from, the one you loved.
Your mother thought you had nine lives, nine close
brushes with death.
Each close call, a lesson. A hand that would lead you out of the
darkness and into the dynamo of eternal light. The world profits
from the mystery of your mind,
Upon your imagination we stand.
2026-06-29 21:00:01
Today, you probably asked a question of a large language model, or accepted a connection suggestion on LinkedIn, or watched a recommended video on YouTube, or took a different route to work based on a traffic prediction from Google Maps. In other words, you probably used artificial intelligence. But what you might not know is how much energy that interaction consumed or why.
AI requires processing massive amounts of data, which is usually done in large data centers populated by thousands of GPUs capable of executing up to trillions of operations per second. But each of those GPUs achieves that by consuming as much as 1,000 watts apiece. For comparison, if you’ve got a newer smartphone, it probably uses less than 1 W. That kilowatt figure puts GPUs on the same level as vacuum cleaners, dishwashers, and stoves, but with the big difference that data-center processors are operating uninterrupted around the clock.
Fundamentally, a lot of this inefficiency is because GPUs are trying to simulate the workings of artificial neural networks using software and billions of transistors, which requires using energy to move massive amounts of data. What’s more, the simulated artificial neurons that make up these networks lack even a fraction of the complex computing behavior of the biological neurons that comprise the most energy-efficient computing system that we know, the human brain.
The brain is roughly one million times as energy efficient at many of the comparable tasks we set for AI. To try to approach these efficiencies, a radically different way of computing called neuromorphic engineering is seeking to build electronic components and circuits that act more like the brain’s neurons and the synapses that connect them.
Huge amounts of work have gone into making electronics operate more like biological neurons and synapses. Some research has focused on developing new, experimental devices, but they aren’t yet reliable enough to be used in large systems. Other efforts aim to implement neurons and synapses by interconnecting many complementary metal-oxide-semiconductor (CMOS) transistors—the workhorses of digital logic—to simulate a single neuron and synapse. But this approach requires so many transistors (and a few bulky capacitors) that it greatly limits the size of the system that can be constructed, making it unclear how such brain-inspired hardware could ever scale up and compete with state-of-the-art GPUs.
But all along there was an artificial neuron and a synapse—each a single device—hiding in plain sight. We found them last year. They were each made possible by an ordinary CMOS transistor—and not even a very good one at that. This is the story of their accidental discovery and their great promise for lowering the environmental footprint of AI.
Modern digital electronics is based on producing and manipulating the ones and zeros of the binary code through the operation of metal-oxide-semiconductor field-effect transistors. MOSFETs have evolved in recent years, but their classic form consists of a piece of silicon that has been doped to contain an excess of either positive (p-type) or negative (n-type) charge carriers. (CMOS logic contains transistors of both types.) The device has two terminals connected to the silicon through regions highly doped with the opposite polarity of the rest of the silicon—the source and the drain. Another terminal, the gate, sits atop the silicon that separates the source from the drain. The gate itself doesn’t connect directly to this silicon, instead resting above a thin layer of insulating dielectric.
Notably, there is a fourth terminal that attaches to the bulk of the silicon; think of this bulk terminal as connecting to the underside of the chip. It doesn’t typically get much attention, but it’s very important to our story.
When voltage is applied at the gate and the bulk terminal is grounded, charge carriers of the same polarity as the source and drain are attracted to the channel region. In the case of an n-type source and drain, that will be electrons; for p-type it will be holes. The presence of these charges forms a conductive channel that reduces the resistance between the source and the drain by several orders of magnitude, and the device switches on. As the voltage at the gate increases, this physical phenomenon produces a current signal that, when plotted against the gate voltage, rises steadily. This response is ideal for logic gates, converters, multiplexers, memories, and other digital circuits. But it is not a good fit for mimicking the behavior of a neuron.
In real neural tissue, brain cells, called neurons, consist of a cell body, a long projection called an axon, and short branching projections called dendrites. The suite of behaviors and computing this collection of components is capable of is rich and broad, but the portion that artificial neural networks hope to copy is this: When the cell body’s voltage is perturbed enough to reach a particular threshold, a self-propagating pulse of voltage, called an action potential, shoots down the axon. The axon terminates in a synapse, an electrochemical connection between the axon and another neuron’s dendrites. The action potential will then temporarily boost the voltage of this next neuron, by an amount that depends on the strength of the synaptic connection. If enough action potentials reach these dendrites in a given time—from this neuron or from others that might also form synapses there—the cell body’s voltage will surpass the threshold and trigger its own action potential.
The unusual action the authors discovered is understandable if you consider that a MOSFET contains a hidden bipolar-junction transistor.
Under normal operation, with the bulk terminal grounded, increasing voltage at the drain leads to current that increases steadily. When the voltage decreases, current follows the same sloped path. Although some pairs of electrons and holes are created by current crashing into silicon atoms, these are swept away before they can accumulate.
Adding resistance to the bulk terminal means these extra holes pile up, increasing the bulk voltage relative to the source. Once that voltage reaches a certain value, the hidden transistor activates, causing current to spike. Current remains high until the drain voltage drops past a certain point.
To get closer to the behavior of real neurons, artificial neurons should produce a current spike when a critical voltage threshold is crossed and then quickly relax back to a resting state on their own. This spike needs to be sudden—nonlinear. It should also exhibit some hysteresis; that is, the activation and relaxation voltages should be different from each other to ensure that current flows only for a certain amount of time.
What’s wanted from an artificial synapse, the thing that connects two artificial neurons, is less complicated, but equally important. The main thing is that its conductance can be electronically adjustable. The device’s conductive states should increase and decrease in a linear pattern and remain stable over time.
No single MOSFET working under the standard operation mechanism can reproduce either of these neural properties. Instead, it’s been done by combining them into complex circuits. Until now, each neuron and each synapse has been implemented by interconnecting dozens and sometimes even hundreds of MOSFETs, which is highly inefficient in terms of area, performance, and cost. To limit the amount of space needed, chips can multiplex their signals, sending them to neurons and synapses serially, but such sequential processing introduces additional delays.
Despite these area-and-time penalties on tasks such as audio processing, computer vision, or health monitoring, state-of-the-art brain-inspired microchips have achieved power reductions up to a thousandfold compared with those of GPUs or CPUs on the same task. If we could create neurons and synapses from individual devices that are readily manufacturable instead, we might target more massive implementations while maintaining energy efficiency.
Working in our laboratory in 2024, one of my students was measuring a memory circuit that consisted of one transistor and one memristor—a type of nonvolatile memory device first fabricated in 2008. The student’s memristor circuit was built from two-dimensional material atop a silicon microchip containing MOSFETs. The MOSFETs were created in a commercial foundry using fabrication technology called the 180-nanometer node, which was cutting-edge in the year 2000.
One day the student forgot to connect the bulk terminal of the transistor. What he observed was a sudden increase in current with high nonlinearity that self-relaxed when the voltage was ramped down (a phenomenon called a hysteresis loop). This was a very promising neuronlike behavior!
After a fruitless week of trying to think of an explanation for this behavior, I (Lanza) asked Pazos, then my postdoctoral fellow, to try to observe and control this phenomenon in chips without memristors. This time, we applied pulses of voltage—like the spikes a neuron would produce—instead of the ramped voltage that my student used when he first saw the peculiar behavior.
Pazos’s new data helped us understand what was going on. The key was that oft-ignored fourth, or bulk, terminal of a MOSFET. Under ordinary operation, many mobile charge carriers flitting through the channel collide with the silicon atoms, producing free pairs of electrons and holes—a process known as impact ionization. The electric field created by the potential difference between the source and the drain causes these new free electrons to drift toward the positively biased drain and the holes to move toward the bulk terminal, which is usually grounded, removing the charge without any drama.
However, when the bulk terminal of the transistor is floating—unconnected as it was in my student’s experiment—the holes produced by impact ionization cannot be driven to the ground. Instead, they accumulate in the bulk of the silicon, increasing its voltage. Then things start to get interesting.
It helps here to imagine a MOSFET as two different kinds of transistors occupying the same physical space—the intentionally constructed MOSFET and a hidden, bipolar junction transistor. A bipolar device transmits a current signal across two p-n junctions, in this case the interfaces between the source and the channel region and the channel and the drain. This signal is in proportion to a smaller current at a third terminal in between, called the base. In our experiment, that third terminal is the bulk.
To get current flowing through a bipolar transistor, you need a big enough potential difference between the base and one of the other terminals, so that current can get across the p-n junction. Let’s say this “threshold voltage” is 0.7 volts, although the real number depends on device geometry and silicon doping. In our device, that potential difference comes from those holes that were accumulating in the bulk, because it was not connected to ground. Once it reaches the threshold voltage, the device becomes sharply conductive, producing an abrupt increase of current. This sharp current increase eventually falls off once the drain voltage is lowered, because that lowering reduces the rate at which holes are generated in the bulk. The remaining excess holes recombine with stray electrons or leak away, and finally the bulk voltage falls. This cycle of hole accumulation, current spike, and hole removal gives rise to a hysteresis loop, very much like the electrical behavior of a biological neuron as it integrates ionic currents, fires a spike, and relaxes back to its resting voltage.
Initially, we observed this behavior only in a few transistors, and the relaxation time was very different for each of them. So, to try to control it better, we adjusted the resistance of the bulk terminal using a second MOSFET. Simply setting that resistance suddenly caused all the transistors to fire at the same voltage with hardly any variability. In other words, we found we could create perfect electronic neuron behavior in a single silicon transistor by controlling the bulk contact resistance. Setting the resistance can be done by doping the silicon during fabrication, but we think the two-transistor cell—where one acts as the bulk resistance—offers much greater versatility because it allows for electronic control.
We had to make sure the phenomenon would last, otherwise such a device would be useless. To our delight, every single one of the devices we tested worked over 10 million cycles. Not even one of them failed during our tests.
To be honest, we were amazed. Dozens of research groups and companies all around the world have spent many millions of U.S. dollars over the past 20 years trying to emulate these neural behaviors using experimental memristor-like devices and other things, with limited success, mainly due to reliability and cost issues. We managed it in the cheapest and most industry-standard device: the MOSFET. This result was so shocking that we decided to confirm it using microchips from a different foundry. It was successful: All the behaviors could be reproduced, and perfect yield was achieved once again.
We were happy with the results and had started the process of filing for a patent and writing up our findings for the journal Nature, when our lab made another astonishing discovery: The same kind of MOSFET could act as a synapse, too!
Recall that in ordinary operation some electrons crash into silicon atoms to create pairs of electrons and holes. We noticed that at specific values of bulk resistance a significant amount of the charge from this impact ionization would get trapped in the gate dielectric. This trapped charge interferes with the flow of current through the MOSFET, effectively changing the device’s conductance. Importantly, this new conductance is stable and adjustable at will. It was then that we realized the MOSFET could also be used as an electronic synapse.
As it was in the neuron transistor, the bulk terminal was the key. A negative bulk-source voltage drives electrons into the dielectric, decreasing conductance. A positive one pushes holes in, increasing it.
Here’s how the MOSFET synapse and the MOSFET neuron, together called a neurosynaptic random-access memory, or NSRAM, could work together to achieve a simple neural circuit: Say you had a circuit consisting of three synapse MOSFETs and a neuron MOSFET. The synapses have already been programmed as we’ve described, so that each has a different conductance. Spikes of voltage with different patterns and frequencies are applied to the gate of each of these transistors. What emerges from their drains are spikes of current with amplitudes modulated by the synapses conductance values.
The spikes converge at the drain of the neuron MOSFET. With each spike, impact ionization causes charge to build in the bulk of the silicon. Some of it will drain away, but if enough spikes arrive in a short enough period of time, the bulk voltage will reach a value at which the “hidden” transistor triggers a spike of current through the MOSFET. This current would then go on to become the input to other MOSFET synapses, and so on. The behavior is exactly the kind of integrate-and-fire action real neural circuits deliver.
The competitive advantage of our single-MOSFET electronic neurons and synapses is straightforward: We can produce with only one or two transistors the electronic signals that today require, at an industrial level, dozens and sometimes even hundreds of components. And moreover, unlike other emerging technologies, our solution is fully compatible with today’s silicon manufacturing lines and exhibits a yield of 100 percent in key figures of merit with near-zero variability.
Building functional circuits for brain-inspired computing and AI based on this technology is as exciting as it is laborious. It will require us to improve our computer models to resemble the behavior of both devices more accurately and to do so with computational efficiency. We must also perform accurate circuit- and system-level simulations to validate computing architectures, design peripheral circuitry to drive and convert signals, and undergo multiple fabrication rounds to optimize performance.
But all that will be worthwhile, because it could result in brain-inspired microchips for AI with better energy efficiencies than what we have now. These chips will first be a fit for smaller-scale, “edge-AI” tasks, such as bringing greater intelligence to battery-powered systems. But if we can scale up such chips, maybe in the long run they can compete with state-of-the-art GPUs.
2026-06-29 19:00:02
In 1839, J.M.W. Turner painted The Fighting Temeraire. The old warship, once a hero of the Battle of Trafalgar in 1805, glides like a ghost across the canvas, towed by a small steam tug belching smoke on its final voyage to the ship-breakers. The image shows a clear moment of change: sail giving way to steam, and with it, a major shift in power. The ship relied on timber, rope, canvas, and Britain’s seafaring towns. The tug depended on coal mines and iron foundries that supplied machine shops in the Midlands. Turner showed the tension of this time, when new technology changed who held power.
By Turner’s time, the United States had already defeated Britain’s navy in two wars—one for liberty on land, another for freedom of the seas. The 13 colonies used new technology in creative ways to win their freedom, and by keeping up with innovation, they managed to defend their freedom. Now, as the U.S. celebrates its 250th anniversary, we can ask: What does it really mean for a country to be independent?
We tend to focus on how nations and individuals defend freedom but rarely turn that focus to the tools and systems that sustain freedom. Declaring independence is only the beginning: Independence must still be engineered.
Long before the first shots were fired at Lexington and Concord in 1775, Britain had drawn the lines of conflict through technology. The Wool Act of 1699 choked colonial textile exports. The Hat Act of 1732 crushed local hat-making. The Iron Act of 1750 forbade finished iron goods. Each statute tightened the knot: Colonial capability existed only at Britain’s discretion. The Boston Tea Party may have been a loud response, but resistance also took subtler, more empowering forms. At a 1769 Virginia ball, more than a hundred women arrived in homespun gowns. Every thread was defiance.
When war came, everyday tradespeople pivoted to the fight. Farmers turned plowshares into gun barrels, while clockmakers turned their precision skills to making firing mechanisms. By 1777, two weapons production models had emerged—centralized sites like the Springfield Armory that could produce high-quality guns in large quantities, and household workshops that were more agile and could meet local needs. In parallel, the new nation developed an equally important source of supplies and support: France sent gunpowder and loans and eventually opened a second naval front in 1781, which proved as decisive as any weapon.
After the war, the young republic pursued industrial strength with the same resolve it had shown in battle. In 1789, Samuel Slater arrived from England with textile-spinning technology that he’d memorized, sowing the seeds of U.S. manufacturing, whose early growth rested on domestic cotton, slave labor, and copied techniques. By 1816, gun manufacturer Simeon North’s milling machines were producing interchangeable metal parts, allowing the armed forces to cannibalize parts. In 1822, Thomas Blanchard’s copying lathe automated the shaping of gunstocks. In the 1830s, the federal government imposed tariffs that shielded infant industries, fulfilling Alexander Hamilton’s vision for industrial policy: Build capacity first, then compete.
At the 1851 Great Exhibition in London, American revolvers and reapers with swappable parts stunned international observers. By the 1860s, land-grant colleges were spreading technical education across the nation. Engineering moved into the mainstream, from niche to national necessity, driving broad, though uneven, prosperity. As the Industrial Revolution bloomed, the early U.S. focus on industrial capacity via farms, factories, and formidable wealth positioned the country to compete with the most advanced industrial powers in the world.
For nearly two centuries, that ethos endured, with government-guided infrastructure and markets deciding the details. But around the U.S. bicentennial, in 1976, a conviction took hold across party lines. Finance began to outrank fabrication, and Wall Street prioritized futures contracts over companies owning the factories that made up their supply chains. Domestic factories closed or moved offshore, and companies turned to just-in-time manufacturing and shipping, ostensibly as a way to save on costs. Shipbuilding felt this shift as much as any industry. Shipyards closed, and suppliers of specialized castings and components disappeared along with them, as did skilled technical workers who retired without replacement. Now the U.S. Navy struggles to build submarines fast enough to replace its aging fleet.
Other changes took hold, among them the idea that the company that builds your tractor or medical equipment could prevent you from fixing it yourself. Invasive “terms of service” prevented customers from reaching for a wrench, instead allowing companies to keep reaching into customers’ pockets. These changes are symptoms of both structural and infrastructural fragility. When we lose the ability to understand and sustain the systems we rely on, we lose control—bit by bit.
No nation can build everything alone, of course. From hand-forged muskets to finely printed microchips, the sovereignty etched into our tools demands a prudent calculus: what to make at home, what and with whom to trade. Engineering is how a nation keeps its independence alive. Independence requires both the courage to innovate and the stewardship to maintain what has been built. The American Revolution was itself an act of engineering—daring in vision and deliberate in pairing anvil and alliance. Generations later, can a nation that cannot see its own dependencies, build and maintain its critical tools, or repair what breaks still call itself free?
Turner’s Snow Storm—Steam-Boat off a Harbour’s Mouth, completed three years after The Fighting Temeraire, captures this part of the story. Sea and sky dissolve into a churning vortex around the ship. Turner claimed he had himself lashed to the ship’s mast for four hours so that he could paint the sensation of standing inside a system too vast and tangled to comprehend. A nation that loses sight of what it depends on stands there too: lashed to nothing except the churn.