Fast characterization of multiplexed single-electron pumps with machine learning

TL;DR

This paper introduces a machine learning framework that significantly accelerates the tuning and characterization of multiplexed single-electron pumps, reducing measurement time and enabling scalable device analysis.

Contribution

It presents an active learning-based measurement method that decreases measurement points and time, facilitating large-scale characterization of single-electron pump arrays.

Findings

Reduced measurement points by about tenfold

Achieved eightfold decrease in characterization time

Successfully characterized 28 devices in a multiplexed array

Abstract

We present an efficient machine learning based automated framework for the fast tuning of single-electron pump devices into current quantization regimes. It uses a sparse measurement approach based on an iterative active learning algorithm to take targeted measurements in the gate voltage parameter space. When compared to conventional parameter scans, our automated framework allows us to decrease the number of measurement points by about an order of magnitude. This corresponds to an eight-fold decrease in the time required to determine quantization errors, which are estimated via an exponential extrapolation of the first current plateau embedded into the algorithm. We show the robustness of the framework by characterizing 28 individual devices arranged in a GaAs/AlGaAs multiplexer array, which we use to identify a subset of devices suitable for parallel operation at communal gate voltages. The method opens up the possibility to efficiently scale the characterization of such multiplexed devices to a large number of pumps.