Superinductor built in a standard silicon chip process using titanium nitride

A team led from nanoGUNE's Quantum Hardware group has built a nonlinear superinductor inside a silicon integrated circuit made with an industrial CMOS process. The device relies on titanium nitride (TiN), a superconducting material, and the researchers used it to make a compact radiofrequency single-electron transistor, a sensor commonly paired with semiconductor spin qubits. The study, with Thomas H. Swift as first author, appears in Nature Electronics.

Why superinductors matter

A superinductor is a circuit element engineered to have an inductance far larger than conventional coils can offer. In quantum circuits, and superconducting qubits in particular, this has real consequences. A very large inductance gives a circuit a very high impedance and a very small inductive energy scale, and both change how the circuit behaves at the quantum level.

Such components are usually made in specialised research fabrication. Producing them with the same processes used for mass-produced chips would make it easier to place them next to the transistors and wiring that a larger quantum system also needs.

Using titanium nitride in a CMOS process

The authors report that superconducting circuit elements can be fabricated within a commercial CMOS process by using TiN. The material has a high kinetic inductance. This term refers to the inductance that comes from the inertia of charge carriers in a superconductor, and in suitable thin films it can far exceed the inductance set by geometry alone. In practice, a designer can then obtain a large inductance from a small structure without losing the superconducting properties of the film.

The team's device is described as nonlinear, meaning its response changes with the current passing through it. This is a property that quantum circuit designers often seek, since linear elements alone cannot produce the energy-level structure that qubits require.

A compact sensor for spin qubits

The researchers used the superinductor to build a radiofrequency single-electron transistor. This type of sensor detects tiny changes in charge and is widely used to read out semiconductor spin qubits. According to the study, the new version combines a small footprint with high sensitivity.

The link between inductance and sensing is straightforward in principle. Readout of this kind typically places the sensor in a resonant circuit, and a resonator with a large inductance can be matched well to high-impedance devices. Keeping the inductor on the chip, rather than in separate components, also reduces the space the readout needs. The source does not give quantitative performance figures, so the size of the sensitivity gain cannot be judged from this summary alone.

What comes next

The next step is to integrate the resonator with multi-gate transistors. The aim is to demonstrate spin readout that is both fast and accurate, well below the microsecond timescale. Until that demonstration is made, the benefit for qubit readout remains a prospect rather than a result.

The authors also see uses beyond spin-qubit sensing. They suggest that TiN-based CMOS inductors could contribute to radio astronomy, low-power cryogenic electronics, and quantum sensing and simulation. These applications are put forward as possibilities, not shown in the work.

For researchers in quantum hardware, the main point is the fabrication route. Showing that a high-kinetic-inductance superconductor can be integrated in a standard silicon process addresses a practical barrier to scaling, namely the gap between laboratory-made quantum components and the manufacturing infrastructure of the semiconductor industry. The contributing institutions are nanoGUNE, Quantum Motion, the London Centre for Nanotechnology, Imperial College London and the University of Cambridge.

The paper is titled "A nonlinear superinductor in a deep-submicrometre integrated circuit" (DOI: 10.1038/s41928-026-01728-8).

Source: A new superinductor created within a silicon integrated circuit (Phys.org, 5 November 2026).

Image: AI-generated illustration.