L
Lumdo Chen
Researcher at National Taiwan University
Publications - 5
Citations - 131
Lumdo Chen is an academic researcher from National Taiwan University. The author has contributed to research in topics: Singular value & Spatial correlation. The author has an hindex of 4, co-authored 5 publications receiving 129 citations. Previous affiliations of Lumdo Chen include United Microelectronics Corporation.
Papers
More filters
Journal ArticleDOI
Full-Chip Routing Considering Double-Via Insertion
TL;DR: A new full-chip gridless routing system considering double-via insertion for yield enhancement and a new redundant-via aware detailed maze routing algorithm (which could be applied to both gridless and grid-based routing).
Proceedings ArticleDOI
Novel full-chip gridless routing considering double-via insertion
TL;DR: An optimal double-via insertion algorithm is developed for the cases with up to three routing layers and the stack-via structure, and then extended to handle the general cases, which significantly improve the via count, the number of dead vias, double- Via insertion rates, and running times.
Proceedings ArticleDOI
Process-Variation Statistical Modeling for VLSI Timing Analysis
TL;DR: Experimental results show how the methods and new algorithms presented expose some enhancements in both accuracy and versatility of the quadratic Gaussian polynomial model to resolve difficulties in SSTA.
Proceedings ArticleDOI
Accurate and analytical statistical spatial correlation modeling for VLSI DFM applications
TL;DR: A novel spatial correlation modeling method, based on Singular Value Decomposition (SVD), can generate sequences of polynomial weighted by the singular values that can capture the general spatial correlation relationship.
Journal ArticleDOI
Accurate and Analytical Statistical Spatial Correlation Modeling Based on Singular Value Decomposition for VLSI DFM Applications
TL;DR: A novel spatial correlation modeling method that can not only capture the general spatial correlation relationship but also can generate highly accurate and analytical models is proposed, based on singular value decomposition.