XSD: PtSi Bridges Superconductivity with Established CMOS Processing

A material already integral to modern electronics is now showing promise for quantum computing; platinum silicide, or PtSi, has been confirmed as a superconductor and is being investigated for use in advanced quantum devices. Dr. Tharanga Nanayakkara has detailed a method for synthesizing superconducting PtSi thin films using magnetron sputtering and rapid thermal annealing, a process that establishes the material as a silicon-compatible platform for quantum information technologies. This research focuses on bridging the gap between superconducting materials and established CMOS processing, potentially enabling monolithic integration with existing semiconductor platforms. “While aluminum, niobium, and more recently tantalum have demonstrated success in superconducting quantum circuits,” said Nanayakkara, “there remains strong interest in identifying alternative materials that can enable large-scale integration with silicon-based technologies,” suggesting a path toward scalable quantum technology by leveraging existing manufacturing infrastructure.

PtSi, a transition-metal silicide routinely employed in semiconductor fabrication, surprisingly exhibits superconducting properties and is now under investigation for advanced quantum devices such as microwave kinetic inductance detectors. Dr. These films underwent rigorous structural, chemical, and low-temperature electrical characterization to confirm their superconducting behavior and compatibility with silicon-based systems, establishing PtSi as a viable platform for quantum information technologies. The research extends beyond simply demonstrating superconductivity in PtSi; nanoscale superconductor-constriction-superconductor Josephson junctions and dc superconducting quantum interference devices, or SQUIDs, were fabricated and tested at cryogenic temperatures.

Through magnetic-field-dependent transport measurements combined with Ginzburg-Landau simulations, researchers extracted current-phase relations, revealing strongly skewed behavior as constriction dimensions neared the superconducting coherence length. “These studies establish PtSi as a silicon-compatible superconducting platform suitable for quantum information technologies,” stated Dr. Nanayakkara, highlighting the material’s potential for integration. Collaborative work focused on tantalum microwave resonators and nitride nanowires, fabricated using 300 mm semiconductor manufacturing, underscores the broader importance of materials engineering and interface control for cryogenic quantum technologies. The ability to leverage existing CMOS processing for quantum device fabrication represents a significant step toward scalable quantum systems, potentially reducing the cost and complexity associated with building quantum hardware.

Research into platinum silicide (PtSi) extends beyond its established role in conventional semiconductor manufacturing; Dr. This skewing, alongside the influence of kinetic inductance, dictates the overall nonlinear response of superconducting circuits, offering a new avenue for manipulating quantum information. “By combining magnetic-field-dependent transport measurements with numerical Ginzburg-Landau simulations, we extracted the current-phase relations of individual constriction junctions and quantified their nonlinear behavior,” explained Dr. Nanayakkara, detailing the methodology used to understand these complex interactions.

By combining magnetic-field-dependent transport measurements with numerical Ginzburg-Landau simulations, we extracted the current-phase relations of individual constriction junctions and quantified their nonlinear behavior.

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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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