Clustering algorithms have been explored in recent years to solve hotspot clustering problem in Integrated Circuit design. With various applications in Design for Manufacturability flow such as hotspot library generation, systematic yield optimization and design space exploration, generating good quality clusters along with their representative clips is of utmost importance. With several generic clustering algorithms at our disposal, hotspots can be clustered based on the distance metric defined while satisfying some tolerance conditions. However, the clusters generated from generic clustering algorithms need not achieve optimal results. In this paper, we introduce two optimal integer linear programming formulations based on triangle inequa...
This dissertation focuses on two optimization problems that arise in network-based data mining, conc...
A data set may contain of one or more 'clouds' of data objects. The task for cluster analysis is, to...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
Clustering algorithms have been explored in recent years to solve hotspot clustering problems in int...
Clustering or cluster analysis [5] is a method in unsupervised learning and one of the most used tec...
Detection of process sensitive patterns known as hotspots is critical to maximising yield in integra...
The final objective of an integrated circuit design is to produce a layout, that is, a geometrical r...
We address the problem of building a clustering as a subset of a (possibly large) set of candidate c...
In this study, we work on a clustering problem where it is assumed that the features identifying the...
As the modern integrated circuit continues to grow in complexity, the design of very large-scale int...
We introduce the K-way Thermal Chip Clustering (KT2C) algorithm; a VLSI chip partitioning algorithm ...
This paper proposes a new design check system that works in three steps. First, hotspots such as pin...
Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, pri...
Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlie...
We consider the following general graph clustering problem: given a complete undirected graph G=(V,E...
This dissertation focuses on two optimization problems that arise in network-based data mining, conc...
A data set may contain of one or more 'clouds' of data objects. The task for cluster analysis is, to...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...
Clustering algorithms have been explored in recent years to solve hotspot clustering problems in int...
Clustering or cluster analysis [5] is a method in unsupervised learning and one of the most used tec...
Detection of process sensitive patterns known as hotspots is critical to maximising yield in integra...
The final objective of an integrated circuit design is to produce a layout, that is, a geometrical r...
We address the problem of building a clustering as a subset of a (possibly large) set of candidate c...
In this study, we work on a clustering problem where it is assumed that the features identifying the...
As the modern integrated circuit continues to grow in complexity, the design of very large-scale int...
We introduce the K-way Thermal Chip Clustering (KT2C) algorithm; a VLSI chip partitioning algorithm ...
This paper proposes a new design check system that works in three steps. First, hotspots such as pin...
Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, pri...
Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlie...
We consider the following general graph clustering problem: given a complete undirected graph G=(V,E...
This dissertation focuses on two optimization problems that arise in network-based data mining, conc...
A data set may contain of one or more 'clouds' of data objects. The task for cluster analysis is, to...
We review the time and storage costs of search and clustering algorithms. We exemplify these, based ...