Carrier and Phonon Dynamics in Multilayer WSe2 captured by Extreme Ultraviolet Transient Absorption Spectroscopy
Juwon Oh, Hung-Tzu Chang, Christopher T. Chen, Shaul Aloni, Adam, Schwartzberg, Stephen R. Leone

TL;DR
This study uses extreme ultraviolet transient absorption spectroscopy to investigate carrier and phonon dynamics in multilayer WSe2, revealing ultrafast processes and element-specific signals crucial for electronic and photonic applications.
Contribution
It introduces a novel XUV transient absorption approach to simultaneously monitor holes and phonons in multilayer WSe2, advancing understanding of ultrafast dynamics in layered materials.
Findings
Hole relaxation time: 0.4 ps
Carrier recombination time: 1.5 ps
Phonon heating time: 1.7 ps
Abstract
Carrier and phonon dynamics in a multilayer WSe2 film are captured by extreme ultraviolet (XUV) transient absorption (TA) spectroscopy at the W N6,7, W O2,3, and Se M4,5 edges (30-60 eV). After the broadband optical pump pulse, the XUV probe directly reports on occupations of optically excited holes and phonon-induced band renormalizations. By comparing with density functional theory calculations, XUV transient absorption due to holes are identified below the W O3 edge whereas signals at the Se M4,5 edges are dominated by phonon dynamics. Therein, 0.4 ps hole relaxation time, 1.5 ps carrier recombination time, and 1.7 ps phonon heating time are extracted. The acquisition of hole and phonon-induced signals in a single experiment can facilitate the investigation of the correlations between electron and phonon dynamics. Furthermore, the simultaneous observation of signals from different…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsChalcogenide Semiconductor Thin Films · 2D Materials and Applications · Machine Learning in Materials Science
