Automated Scanning Probe Fabrication for Atomic Scale Devices

With our increasing dependence on technology, the total energy consumption from electronic devices for computation is projected to surpass all other contributions. By creating atomic-scale devices at the fundamental limits in size and energy cost, we can reduce their overall energy consumption while increasing computational power. While proof of concept devices are already routinely created, a fully automated fabrication procedure is necessary to successfully merge this technology with current electronic manufacturing processes. By employing machine learning techniques, the successful implementation of a fully autonomous fabrication system will enable the high volume fabrication and development of these next generation atomic devices. These machine learning techniques will rely on state-of-the-art unsupervised and reinforcement learning techniques which will be used for developing a fully self-sufficient,automated fabrication process of these atomic scale devices, as well as to optimize and enhance their design and operation.

Faculty Supervisor:

Robert Wolkow;Mauricio Sacchi

Student:

Jeremiah Croshaw

Partner:

Quantum Silicon

Discipline:

Physics / Astronomy

Sector:

Manufacturing

University:

University of Alberta