Periodic Reporting for period 3 - SPINAPSE (Creating complexity: toward atomic spin-based neural hardware)
Okres sprawozdawczy: 2022-03-01 do 2023-08-31
The overall objectives of this program is to explore how magnets scaled to the limit of an individual atom can be used to create brain-inspired hardware. The project takes a bottom-up approach, aimed at creating new experimental insight into these physics questions. The bottom-up approach is based on using individual atoms, and controlling how they interact with each other. The original project takes the three most pervasive neuromorphic models used today, and set out to understand how these behavior of interacting magnets can mimic those models. The objectives of the program were coupled with creating a new state of the art in atomic imaging, where the highest resolution microscope that combines magnetic imaging with the highest spatial, energy, and temporal resolutions available today.
In a second endeavor, we developed an atomic-scale platform to realize the first atom-by-atom neural network. This was based on a discovery in 2018, where we discovered the so-called single atom orbital memory derived from a cobalt atom on black phosphorus. In this program, we found that arrays of closely placed cobalt atoms “talk” to each other, and these interactions lead to an energy landscape the identically mimics an attractor network. Using a concept of separation of time scales, we were able to show the fabrication of an atomic synapse coupled to a chain of neurons, the smallest such neural network to date and contained in one material system. Using this discovery, we showed that this new materials platform could be used to mimic a seminal model in machine learning: the Boltzmann machine. Its novelty is not only in its size scale, but also that we have found evidence of self-adaption in this material. Using this simple model system, we can strive to answer many open fundamental questions linking materials to future paradigms in brain-inspired computing.
In the last half of this project, we aim to understand the role of spin in these systems. We want to combine using our newly time-resolved methods as well as magnetic imaging in the atomic scale systems. In this way, we can explore implemented other machine learning algorithms in atomic scale materials, as well as understanding their time-dependent behavior.