> Academic opportunities > PHD positions

Large scale parametric characterization, variability study and tests of quantum devices at cryogenic temperatures

Technological challenge: Advanced nano characterization (learn more)

Department: Département Composants Silicium (LETI)

Laboratory: Laboratoire Caractérisation Electrique et Fiabilité

Start Date: 01-10-2022

Location: Grenoble

CEA Code: SL-DRT-22-0232

Contact: pierre-andre.mortemousque@cea.fr

Context: A natural way to address the scalability of quantum devices is to design and realise arrays of individual quantum objects with nearest-neighbor interaction. In large-scale semiconductor quantum processors, a quantum bit is encoded in the spin of an isolated electron, trapped in an array of quantum dots (QDs) [1]. Over the years, we have studied devices with an increasing number of QDs, in designs that allow for the coherent control of individual spin. However, there is a pre-requisite for a precise control of spin qubits: a deep knowledge of the quantum dots used to confine the electrons in arrays [2]. Therefore, demonstrating the extensive and scalable characterization and calibration of QD systems is crucial for the development of our quantum processor. Last year CEA-Leti has acquired a unique automatised measurement tool for 300-mm wafers at cryogenic temperature, which gives us a new way to develop intelligent and efficient characterization techniques. Objectives and means available: The systematic characterization of the QD control parameters scales with the number of control knobs and QDs. The dense exploration of the parameter space to determine the array's charge state limits the experimental throughput of relevant data points. The student will develop QD characterization algorithms, like adaptive meshing or feature recognition using signal and image processing algorithms to optimise the parameter space exploration. Consequently, within this project, the student will develop feedback loop in the computer software fast real-time processing to optimise the characterization speed and allow for statistical analyses. Parallel to this, the student will work on the development, based on existing works, of optimized experimental characterization protocols. Indeed, the characterization of quantum devices at large scale will build up a statistical knowledge (variability) of the structures that can enhance the measurement protocols. As such, they will be embedded in the automated characterization workflow for arrays of quantum dots and will progressively enrich the physical modelling and understanding of the QD arrays. [1] Vinet, M. et al. Towards scalable silicon quantum computing, IEDM (2018). [2] Mortemousque, P.-A. et al. Coherent control of individual electron spins in a two-dimensional quantum dot array. Nat. Nanotechnol. (2020) doi:10.1038/s41565-020-00816-w

See all positions Download the offer (.zip)

Email Bookmark and Share