Optimization of Quantum-dot Qubit Fabrication via Machine Learning

TL;DR

This paper presents a machine learning approach using convolutional neural networks to optimize quantum-dot qubit fabrication by analyzing micrographs, improving process robustness, and reducing development time.

Contribution

It introduces a novel high-throughput machine learning method for optimizing nanofabrication processes in quantum computing.

Findings

Effective interpretation of micrographs for device quality assessment

Optimization of lithographic process within a five-dimensional design space

Demonstration of addressing lithographic proximity effects

Abstract

Precise nanofabrication represents a critical challenge to developing semiconductor quantum-dot qubits for practical quantum computation. Here, we design and train a convolutional neural network to interpret in-line scanning electron micrographs and quantify qualitative features affecting device functionality. The high-throughput strategy is exemplified by optimizing a model lithographic process within a five-dimensional design space and by demonstrating a new approach to address lithographic proximity effects. The present results emphasize the benefits of machine learning for developing robust processes, shortening development cycles, and enforcing quality control during qubit fabrication.