/Compact modeling Si quantum dot qubits for large scale quantum processors

Compact modeling Si quantum dot qubits for large scale quantum processors

Leuven | More than two weeks ago

Explore and generate advanced compact models that summarize the intricate device physics of state of the art quantum dot qubits.

Compact modeling Si quantum dot qubits for large scale quantum processors

 

Silicon quantum dot spin qubits are amongst the most promising candidates for large scale quantum processors, in view of their compact size, compatibility with advanced CMOS technology, and long lived coherence times as well as high fidelity control. At imec, we are developing state of the art silicon quantum dot qubits, with ultralow noise and record high fidelities, using industrial scale 300 mm fabrication technology (Elsayed et al., NPJ Quantum 10, 70, 2024; Dumoulin-Stuyck et al, IEEE VLSI Silicon workshop, 2024), resulting in some of the largest quantum dot arrays present worldwide. These efforts are made possible by dedicated optimization efforts that focus on ultra-low noise gate-stacks, clean and minimally defective substrates and epitaxial growth, as well as control and connection modules, constantly improving the technology baseline.

 

Upscaling good qubits to large scale quantum processors requires a concerted effort between further technology advancement, design and measurement of the systems, and feedback of the latter into further improvement of the former – with dedicated qubit models as the main translation instrument between fabrication and quantum design and measurement. Those models become crucial in the design phase of larger scale quantum processors, and will be key to a fast closure of the design-fabrication-device feedback loop. Such feedback loops are well known in the semiconductor industry, where Technology Computer Aided Design (TCAD) is used extensively.

 

As quantum bits are highly sensitive to their environment, TCAD for quantum bits will require dedicated models covering the detailed device physics from a quantum perspective, to be used in a design context. The dynamic range of the modelling effort therefore goes well beyond that of classical TCAD, while aspiring a similar functionality. In this PhD topic, you will work with the quantum device specialists, fabrication specialists and measurement engineers to investigate the performance limiting physics of the quantum dot qubits and blend them into effective models that can be used at design phase, relying on the high reproducibility of the imec devices. You will both study and model the physical origins of disorder and noise, potential spatial, temporal and spectral correlations between them, and feed back that knowledge into designs that can be used to probe the limits of larger scale systems.   

 

What you will do:

  • Explore/generate advanced compact models that summarize the intricate device physics of state of the art quantum dot qubits.
  • Work with integration specialists and cryogenic quantum device engineers to help validate the models based on experimental data.

Who you are: 

  • You have a Master’s degree in electrical engineering, microelectronics, applied physics, computational physics, physics, or related fields.
  • You would like to explore new challenges in engineering large scale-quantum processors.
  • You have a keen interest in modelling, theory as well as the experimental context in which models are applied.
  • You are self-motivated, like to take initiative, and a team-player.
  • Given the international character of imec, a fluent knowledge of English is necessary.

 

Type of work: 70% modelling, 20% data analysis, 10% literature research


Daily advisors: Dr. Sofie Beyne (Sofie.beyne@imec.be)

Supervisor: Prof. Kristiaan De Greve (kristiaan.degreve@imec.be)


Required background: Master’s degree in electrical engineering, microelectronics, applied physics, computational physics, physics, or related fields.

Type of work: 70% modelling, 20% data analysis, 10% literature research

Supervisor: Kristiaan De Greve

Daily advisor: Sofie Beyne

The reference code for this position is 2025-074. Mention this reference code on your application form.

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