EDBT 2026 Demo / reviewers in the wild / expert
Thomas J. T. Kwan
dblp:44/6649
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Electronic design automation · 67% Performance modeling and evaluation · 33% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Electronic design automation › technology computer-aided design
device simulation |
0.0 | 1 | 1998 | Comparison of statistical enhancement methods for Monte Carlo semiconductor simulation · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 1998 |
Electronic design automation › technology computer-aided design › device simulation
monte carlo device simulation |
0.0 | 1 | 1998 | Comparison of statistical enhancement methods for Monte Carlo semiconductor simulation · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 1998 |
Methods — techniques the papers use, named apart from their topics
splitting-gathering · 0.0multicomb · 0.0cloning-rouletting · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1998 | Comparison of statistical enhancement methods for Monte Carlo semiconductor simulationabstractThree methods of variable-weight statistical enhancement for Monte Carlo semiconductor device simulation are compared. The steady-state statistical errors and figures of merit for implementations of the multicomb, cloning-rouletting, and splitting-gathering enhancement methods are obtained for bulk silicon simulations. The results indicate that all methods enhance the high-energy distribution tail with comparable accuracy, but that the splitting-gathering method achieves a lower error at low energies by automatically preserving a peak in the bin populations at the peak of the particle energy distribution. Carl J. Wordelman, Thomas J. T. Kwan, Charles M. Snell |
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. | 2 |