Existence and Use of Large Datasets To Address Research Questions for Characterization and Autonomous Tuning of Semiconductor Quantum Dot Devices

Issuing agencies

Abstract

The National Institute of Standards and Technology (NIST) is seeking input regarding needs and gaps in data-sharing approaches to accelerate innovations in using artificial intelligence and machine learning techniques to improve the experimental characterization and control of semiconductor quantum dot devices. As part of this effort, NIST hopes to identify the needs for quantum dot device tuning automation, including existing and future quantum dot related datasets that may be useful for research, means and methods currently deployed for tuning, barriers for advancing the current state of the art techniques to enable automation of large quantum dot arrays, and the meaningful measures of success for the various stages of characterization and control. NIST plans to hold a workshop on July 19- 20, 2023, in conjunction with this notice. The information received in response to this notice and during the workshop will inform efforts and coordination needed to develop a reference database of experimental and simulated data. The reference database will ideally represent the various phases of tuning quantum dot devices, along with metrics for benchmarking the characterization and control methods for quantum dot devices.