SemiQon develops cryo-optimized CMOS electronics for future computing.

Billions of CMOS transistors underpin every facet of modern digital life, from smartphones to sprawling data centers, yet fifty years of advancements are now colliding with fundamental physical limits at room temperature. SemiQon is responding to this challenge by developing CMOS electronics specifically engineered to operate at cryogenic temperatures, as low as −269°C.

“CMOS is the foundation of virtually all modern electronics. In the same way, cryo-optimized CMOS can become a platform technology for the next generation of computing,” the company states, suggesting a path forward that diverges from solutions ranging from intensive liquid cooling to, as Elon Musk proposed, shipping data into orbit.

CMOS Heat Limits Drive Demand for Advanced Computing

The escalating heat generated by modern processors is now prompting a re-evaluation of fundamental chip design, as even the most advanced semiconductors struggle with energy loss. Every computation, according to experts, inevitably releases energy as heat due to electrical resistance and physical effects within the chip itself. This poses a growing challenge for increasingly powerful AI systems and the data centers that support them, with conventional cooling solutions proving costly and environmentally impactful.

Elon Musk’s proposal to transmit data into orbit represents one extreme response to this issue, highlighting the severity of the thermal limitations. SemiQon is addressing this challenge by focusing on cryogenic temperatures, around −269°C, where materials exhibit altered properties. At these temperatures, electrical resistance in interconnects diminishes, and superconductivity becomes a possibility, potentially leading to significantly more efficient electronic systems.

However, existing CMOS chips are not inherently suited for such extreme conditions. They require specific design and construction optimized for cryogenic operation. The company’s approach centers on developing CMOS electronics engineered to operate reliably and efficiently in these low-temperature environments, extending beyond applications in quantum computing, SemiQon says. This pursuit of cryo-optimized CMOS is rooted in the established foundation of CMOS technology itself. This suggests a potential shift from simply improving room-temperature CMOS to establishing a new platform built for an entirely different operating environment.

The benefits of this approach extend beyond mere efficiency gains; it allows for applications currently unattainable with conventional electronics. The limitations of scaling current CMOS technology at room temperature are becoming increasingly apparent due to fundamental physics constraints. Fifty years of advancements have brought remarkable efficiency, but further improvements are proving difficult.

This is because the inherent resistance within semiconductor devices limits how much power can be processed before excessive heat is generated. SemiQon’s work offers a potential pathway around these limitations by exploiting the unique properties of materials at extremely low temperatures, creating a new frontier for computing innovation. The company believes this redesigned platform will enable applications that are difficult, or even impossible, to achieve with current technology, and is only beginning to explore the full potential of this approach.

CMOS is the foundation of virtually all modern electronics. In the same way, cryo-optimized CMOS can become a platform technology for the next generation of computing.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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