Probing Low-Frequency Charge Noise in Few-Electron CMOS Quantum Dots

Cameron Spence,Bruna Cardoso Paz,Vincent Michal,Emmanuel Chanrion,David J. Niegemann,Baptiste Jadot,Pierre-Andre Mortemousque,Bernhard Klemt,Vivien Thiney,Benoit Bertrand,Louis Hutin,Christopher Bauerle,Maud Vinet,Yann-Michel Niquet,Tristan Meunier,Matias Urdampilleta

Physical review applied（2023）SCI 2区

Univ Grenoble Alpes

Cited 5|Views45

Abstract

Charge noise is one of the main sources of environmental decoherence for spin qubits in silicon, presenting a major obstacle in the path towards highly scalable and reproducible qubit fabrication. Here we demonstrate in-depth characterization of the charge noise environment experienced by a quantum dot in a CMOS-fabricated silicon nanowire. We probe the charge noise for different quantum dot configurations, finding that it is possible to tune the charge noise over two orders of magnitude, ranging from 1 ueV^2 to 100 ueV^2. In particular, we show that the top interface and the reservoirs are the main sources of charge noise and their effect can be mitigated by controlling the quantum dot extension. Additionally, we demonstrate a novel method for the measurement of the charge noise experienced by a quantum dot in the few electron regime. We measure a comparatively higher charge noise value of 40 ueV^2 at the first electron, and demonstrate that the charge noise is highly dependent on the electron occupancy of the quantum dot.

Translated text

Key words

Semiconductor Quantum Dots,CMOS Scaling

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper