Details

Autor(en) / Beteiligte

• Kiraly, Brian
• Knol, Elze J
• van Weerdenburg, Werner M J
• Kappen, Hilbert J
• Khajetoorians, Alexander A

Titel

An atomic Boltzmann machine capable of self-adaption

Ist Teil von

• Nature nanotechnology, 2021-04, Vol.16 (4), p.414-420

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2021

Quelle

Springer Nature - Connect here FIRST to enable access

Beschreibungen/Notizen

• The quest to implement machine learning algorithms in hardware has focused on combining various materials, each mimicking a computational primitive, to create device functionality. Ultimately, these piecewise approaches limit functionality and efficiency, while complicating scaling and on-chip learning, necessitating new approaches linking physical phenomena to machine learning models. Here, we create an atomic spin system that emulates a Boltzmann machine directly in the orbital dynamics of one well-defined material system. Utilizing the concept of orbital memory based on individual cobalt atoms on black phosphorus, we fabricate the prerequisite tuneable multi-well energy landscape by gating patterned atomic ensembles using scanning tunnelling microscopy. Exploiting the anisotropic behaviour of black phosphorus, we realize plasticity with multi-valued and interlinking synapses that lead to tuneable probability distributions. Furthermore, we observe an autonomous reorganization of the synaptic weights in response to external electrical stimuli, which evolves at a different time scale compared to neural dynamics. This self-adaptive architecture paves the way for autonomous learning directly in atomic-scale machine learning hardware.