/Upscaling of silicon spin qubits

Upscaling of silicon spin qubits

PhD - Leuven | More than two weeks ago

Prototyping a scalable quantum processor

The rapid development of quantum computing techniques has attracted great attentions across academic research labs, tech companies, and general public. Towards a large-scale quantum computer, complex structures and full-stack engineering are crucial, which drives a migration of the research efforts from university to big companies and research institutes.  


Spins of electrons are natural qubit systems offering long quantum coherence. The spin of a single electron can be electrically confined in a quantum dot, with a structure similar to a transistor. The compatibility with the readily developed micro-electronics industry makes the silicon spin qubit a very attractive platform for large scaling quantum computing. Imec has demonstrated the integration of silicon qubits with advanced industry manufacturing process (highlight in IEDM2020 and VLIS2021). Moreover, by full gate stack optimization and strain engineering, record best charge noise and cryogenic uniformity has been demonstrated on fundamental qubit structures.


However, for large-scale spin qubit processors, the requirements are stricter than the standard integrated circuits: control signals from DC to few tens of GHz should be delivered to individual qubits; qubit interaction should be accurately tuned (numerically); the qubits are more susceptible to environmental noise; and the device physics at cryogenic temperature is not well understood.  


In this PhD project, you will participate in imec’s silicon quantum computing program to address the challenges of upscaling silicon spin qubits. By leveraging the know-hows of the micro-electronics technology and the optimized fab qubit integrations, you will explore novel implementations towards qubit arrays. Knowledge in condensed matter physics and experience in low-temperature measurements are highly desired. 

Required background: Physics, Engineering Science, or equivalent

Type of work: 60% experiment, 20% literature, 20% modelling/simulation

Supervisor: Kristiaan Degreve

Daily advisor: Roy Li

The reference code for this position is 2023-038. Mention this reference code on your application form.

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