Every probe humanity has sent to Venus has died. The Soviet Venera landers survived between 23 minutes and two hours on a surface where the temperature exceeds 460 degrees Celsius. Their electronics, designed to endure heat that would melt lead, still failed. The longest-lived mission in the history of Venus exploration lasted 127 minutes. Then the chips stopped working and the data stopped flowing.
A team at the University of Southern California has built a memory chip that operates reliably at 700 degrees Celsius, hotter than molten lava, and more than 200 degrees beyond anything Venus could throw at it. The device, published in Science on 26 March 2026, held data for more than 50 hours at that temperature without refresh, survived more than one billion switching cycles, and ran on 1.5 volts with a switching speed measured in tens of nanoseconds. Seven hundred degrees was not the device’s limit. It was the limit of the testing equipment.
The Device
The device is a memristor, a nanoscale component that stores information and performs computation simultaneously. The device that Joshua Yang’s team built at USC consists of three layers: tungsten on top, hafnium oxide ceramic in the middle, and a single-atom-thick sheet of graphene on the bottom. Tungsten has the highest melting point of any metal. Hafnium oxide is a standard insulator in semiconductor fabrication. Graphene is a form of carbon that, like diamond, withstands enormous heat without degrading.
In conventional memory devices, heat causes metal atoms from the top electrode to migrate through the ceramic layer until they reach the bottom electrode, creating a permanent short circuit that kills the device. Graphene prevents this. Its surface chemistry with tungsten is, as Yang put it, almost like oil and water. The tungsten atoms find nothing to anchor them and migrate away. No anchor, no short circuit, no failure.
The team did not merely observe the effect. Using electron microscopy, spectroscopy, and quantum-level computer simulations, they mapped the atomic interface between graphene and tungsten to understand exactly why it works. That mechanistic understanding means other materials with similar surface chemistry can now be identified, potentially making the device easier to manufacture at industrial scale. Two of the three materials, tungsten and hafnium oxide, are already standard in semiconductor foundries worldwide. Graphene is on the development roadmaps of both TSMC and Samsung.
The AI Connection
The extreme-temperature result is the headline, but the commercial significance of the memristor lies elsewhere. More than 92 per cent of the computing in AI systems is matrix multiplication, the core mathematical operation behind everything from image recognition to large language models. Today’s digital processors perform it sequentially, step by step, consuming enormous amounts of energy. A memristor performs it physically. When electricity flows through the device, Ohm’s Law, voltage multiplied by conductance, produces the answer as a current. The multiplication happens in the instant the electricity passes through. No clock cycles. No memory bus. No energy wasted shuffling data between processor and storage.
This is in-memory computing: the data stays where the computation happens, eliminating the von Neumann bottleneck that constrains every conventional processor architecture. The result is inference that is orders of magnitude faster and more energy-efficient than GPU-based systems performing the same calculations.
The International Energy Agency projects that energy use from data centres will double by 2026, driven overwhelmingly by the computational demands of AI training and inference. The AI industry’s answer has been to build larger data centres, secure more power, and negotiate nuclear energy contracts. A memristor-based architecture attacks the problem at a different level: not by supplying more energy to the same kind of chip, but by building a chip that needs orders of magnitude less energy to perform the same computation.
AI demand has driven a 90 per cent surge in memory prices and a global DRAM shortage, forcing manufacturers to redirect capacity toward high-bandwidth memory for AI accelerators. The memristor represents a fundamentally different approach. Instead of separating memory from processing and shuttling data between them at enormous energy cost, it combines them. The architecture does not compete with DRAM for capacity. It competes with GPUs for the AI inference workload itself.
The Company
Yang co-founded TetraMem with three co-authors of the original memristor research: Qiangfei Xia, Miao Hu, and Ning Ge. The company, based in San Jose, has built working in-memory computing chips that students in Yang’s lab use daily to run machine learning tasks. TetraMem has partnerships with SK hynix, the world’s second-largest memory manufacturer, on a joint research project to advance in-memory computing for AI; with Andes Technology to integrate its memristor architecture with a RISC-V vector processor; and with NY CREATES at the Albany NanoTech Complex, where the company successfully upscaled its technology from 200mm to 300mm wafers, the industry-standard platform for mass manufacturing.
The NY CREATES partnership is particularly significant. It was supported under the CHIPS and Science Act’s goal of strengthening the domestic semiconductor ecosystem, and demonstrated what NY CREATES calls a split-fab model: companies develop and test chips at Albany before transferring the processes to a foundry partner for mass production. TetraMem’s memristors are no longer a laboratory curiosity. They are on 300mm wafers.
The US government’s CHIPS Act investments have reshaped the domestic semiconductor landscape, with billions flowing into logic chip fabrication. TetraMem’s path through NY CREATES shows that the Act’s ambitions extend beyond logic: the infrastructure built to reshore chip manufacturing also enables fundamentally new computing architectures to reach production scale.
The Market
The global memristor market was valued at 420 million dollars in 2025 and is projected to reach 4.5 billion dollars by 2030 and 21.7 billion dollars by 2035, growing at a compound annual rate of more than 48 per cent. The broader analog AI chip market is expected to grow from 251 million dollars in 2025 to 2.5 billion dollars by 2035. The numbers are small relative to the 600 billion dollars that Nvidia alone generated in market capitalisation from AI chip demand. But they represent the earliest phase of an architectural transition.
The competitors include Mythic AI, Rain Neuromorphics, and a growing number of research labs at TSMC, Samsung, and KAIST that are building memristor crossbar arrays for edge inference. TSMC’s mixed-precision processor achieved 91.2 per cent array yield and 85 per cent accuracy on standard image classification benchmarks. Asia-Pacific handset manufacturers have committed to embedding analog compute chips in 2026 flagship devices. The technology is moving from papers to products.
The Frontier
The high-temperature version of the memristor opens a category of computing that does not currently exist: on-site AI inference in environments where conventional electronics cannot survive. A Venus lander equipped with memristor-based processors could analyse atmospheric samples, classify geological formations, and make autonomous decisions without transmitting raw data to Earth and waiting for instructions. A geothermal drilling system could process sensor data at depths where the surrounding rock glows red. A nuclear reactor could run diagnostic AI inside its containment vessel.
Researchers have proposed placing data centres in space to address AI’s energy demands, leveraging the vacuum of orbit for cooling and solar energy for power. The memristor inverts the problem. Instead of taking data centres to space, it takes the computation to the environment where the data originates, whether that environment is the surface of Venus, the interior of a jet engine, or the core of a fusion reactor.
NASA’s High Performance Spaceflight Computing processor, built by Microchip Technology, delivers 500 times the performance of current radiation-hardened space chips. But it was designed for the cold vacuum of interplanetary transit, not the furnace of a planetary surface. The memristor survives both. A device rated for 700 degrees is almost indestructible at the 125-degree peaks that automotive computers routinely face, the radiation-heavy environment of deep space, or the thermal cycling of low-Earth orbit.
Europe’s semiconductor sector has called for an immediate Chips Act 2.0 to fund next-generation manufacturing capabilities beyond conventional logic and memory. Memristor-based in-memory computing is exactly the kind of architecture that such investment would support: a European-fabricable technology that does not depend on access to Nvidia’s GPU supply chain or TSMC’s most advanced logic nodes.
Yang was careful not to oversell the timeline. Memory alone does not make a complete computer. High-temperature logic circuits must be developed and integrated alongside it. The current devices were built by hand at sub-microscale in a laboratory. But the missing component has been made. The chip that survived temperatures hotter than lava was an accident. The company that will sell it was not.