Quantum Hardware, Deep Tech

SemiQon’s quantum role in €3.1M cryogenic memory project with Taiwan partners

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November 3, 2026 by Ivy Delaney

SemiQon is participating in the €3.1 million SHIFT project, an international collaboration focused on scaling hafnium-based Integrated Ferroelectric Technology (FeRAM) for cryogenic memory. Led by Dr. Sayani Majumdar, Associate Professor at Tampere University, the project unites Finnish and Taiwanese partners including Applied Materials, ITRI, and Lyontek Inc. “Quantum and cryo-electronics need low-power cryo-optimized memory technology that reduces the communication needed between room temperature and low temperature,” says Dr. Mika Prunnila, Chief Research Officer at SemiQon, as the consortium aims to improve energy efficiency and scalability for quantum computing systems.

SHIFT Project: Scaling Ferroelectric Random-Access Memory (FeRAM) with International Collaboration

SemiQon is expanding its portfolio of cryo-optimized solutions with participation in the €3.1 million SHIFT project, a collaboration with Taiwanese partners to advance cryogenic memory technologies. The consortium, led by Dr. to demonstrate ultra-low-power, high-performance memory at cryogenic temperatures. A key technical goal is direct integration of cryo-optimized FeRAM with cryo-CMOS logic circuits, addressing a critical bottleneck in quantum computer scaling.

SemiQon joins €3.1M Finland–Taiwan project to advance cryogenic memory technologies
SemiQon joins €3.1M Finland–Taiwan project to advance cryogenic memory technologies — Source: semiqon.com

The project’s scope extends beyond quantum applications, exploring potential uses in other extreme-environment computing systems. SemiQon’s CEO, Dr. Himadri Majumdar, anticipates further developments, stating that by bringing memory and processing closer to the quantum chip, SHIFT seeks to streamline quantum computer scaling and broaden the possibilities for data-intensive computing.

Cryogenic memory is another feature we are adding to SemiQon’s cryo-optimized solutions palette, and there will be more to come.

Dr. Himadri Majumdar, CEO at SemiQon
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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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