On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots

TL;DR

This paper develops and evaluates noise estimation methods to optimize the sensitivity of sensor dots in quantum dot systems, enabling better detection of charge and spin states.

Contribution

It introduces a noise estimation approach tailored for 1D conductance data and demonstrates its effectiveness in tuning sensor dot sensitivity.

Findings

Chen et al.'s estimator performs best for this application

Optimized noise estimation improves identification of operating regimes

Algorithm successfully classifies flank-of-interest in measured data

Abstract

In gate-defined quantum dot systems, the conductance change of electrostatically coupled sensor dots allows the observation of the quantum dots' charge and spin states. Therefore, the sensor dot must be optimally sensitive to changes in its electrostatic environment. A series of conductance measurements varying the two sensor-dot-forming barrier gate voltages serve to tune the dot into a corresponding operating regime. In this paper, we analyze the noise characteristics of the measured data and define a criterion to identify continuous regions with a sufficient signal-gradient-to-noise ratio. Hence, accurate noise estimation is required when identifying the optimal operating regime. Therefore, we evaluate several existing noise estimators, modify them for 1D data, optimize their parameters, and analyze their quality based on simulated data. The estimator of Chen et al. turns out to be best suited for our application concerning minimally scattering results. Furthermore, using this estimator in an algorithm for flank-of-interest classification in measured data shows the relevance and applicability of our approach.