2026-02-27 09:03 |
[FZJ-2026-01872]
Journal Article
Krieger, I. ; Sokolowski, M. ; Haags, A. ; et al
Structure analysis of PTCDA/Ag(100) by low-energy electron diffraction and density functional theory
The adsorption geometry of the planar 3, 4, 9, 10-perylene-tetracarboxylic-dianhydride (PTCDA) moleculein the commensurate c(8 × 8) structure on Ag(100) was determined from the analysis of the intensities in low-energy electron diffraction (LEED-IV). Using data from different angles of incidence and optimized computercode, we were able to overcome earlier challenges given by the limitations of the experimental data set and thecalculation times required for the large unit cell with many atoms. [...]
OpenAccess: PDF;
Detailed record - Similar records
|
2026-02-26 13:12 |
[FZJ-2026-01870]
Poster (Other)
Schnorrenberg, K. ; Kessel, D. ; Bühler, J. ; et al
A Framework for Consistent Measurement Workflows across IC Development, Verification and Data Management
2026deRSE26 - 6th conference for Research Software Engineering & 1st Stuttgart Research Software Day, deRSE26, StuttgartStuttgart, Germany, 2 Mar 2026 - 5 Mar 20262026-03-022026-03-05
[10.34734/FZJ-2026-01870]
Modern research laboratories rely on complex measurement infrastructures that integrate a wide range of devices and interfaces.Traditional laboratory processes are often manual and decentralized, leading to errors and increased workload.This project presents a framework that orchestrates the integrated circuits (IC) and laboratory infrastructure used for qubit measurements. It also includes tools for measurement analysis. [...]
OpenAccess: PDF;
Detailed record - Similar records
|
2026-02-26 13:11 |
Detailed record - Similar records
|
2026-02-26 12:30 |
[FZJ-2026-01868]
Poster (After Call)
Aksoy, A. ; Fleitmann, S. ; Bekman, I. ; et al
Embedded Artificial Neural Networks for Energy-Restricted Edge-Computing Applications
2026deRSE26 - 6th conference for Research Software Engineering & 1st Stuttgart Research Software Day, deRSE26 & SRSD1, StuttgartStuttgart, Germany, 3 Mar 2026 - 5 Mar 20262026-03-032026-03-05
[10.5281/ZENODO.18770020]
The development of energy-efficient and fast machine learning methods plays an increasingly important role in experimental physics, where data analysis and control tasks often need to operate under strict resource constraints. In these contexts, machine learning models can automate complex calibration and analysis tasks while enabling on-device data processing close to the experimental sensors.One representative application presented on this poster concerns the automated calibration of semiconductor spin qubits, while the outlook highlights extensions toward edge-computing approaches in detector systems.The automated calibration of quantum dots is a key prerequisite for realizing scalable quantum computers. [...]
OpenAccess: PDF;
Detailed record - Similar records
|
2026-02-26 11:02 |
Detailed record - Similar records
|
2026-02-26 10:54 |
Detailed record - Similar records
|
2026-02-26 10:52 |
Detailed record - Similar records
|
2026-02-26 10:51 |
Detailed record - Similar records
|
2026-02-26 10:51 |
Detailed record - Similar records
|
2026-02-26 10:33 |
Detailed record - Similar records
|
|
|