Electronic population transfer in solids constitutes the core of modern silicon based technology and is thus the cornerstone of machine intelligence and communication technology. Since the discovery of the semiconductor’s ability to rectify electric currents in 1874 the quest for a general understanding of the micro- and macroscopic behavior of electricity has driven efforts in basic science and industry.
Solid materials and their physical properties are the result of a given structural and electronic configuration that both are dictated by the collective interplay between a number of individual particles. Experimentally, structure information is routinely achieved by a variety of diffraction technologies. In contrast to that, the temporal characteristics of electronic processes are subject to speculations since their evolution acts out in attoseconds (10^-18 s), the natural timescale of electronic mechanisms and way beyond the temporal resolution of established techniques.
In the past decade attosecond physics had an impressive impact on basic research and demonstrated its potential to time resolve electron dynamics in a variety of target systems. So far, investigations have been carried out in atoms, molecules and on the surface of solid samples and the results that have been achieved owing to the unprecedented temporal resolution have triggered extensive theoretical efforts to extend the predictive value of the current models for light-matter interaction and intra-atomic electron dynamics.
The proposed project will apply the tools of attosecond metrology developed in the past years to electronic processes inside solids thus opening a completely new field of science. The proposed efforts set out to investigate electron dynamics unfolding in the inner of bulk material with attosecond resolution and will link techniques and competencies developed at the forefront of ultrafast science to an inestimable number of questions with significant technological relevance.
Field of science
- /natural sciences/computer and information sciences/artificial intelligence/machine learning/deep learning
Call for proposal
See other projects for this call