Evolving small-world networks with geographical attachment preference

We introduce a minimal extended evolving model for small-world networks which is controlled by a parameter. In this model the network growth is determined by the attachment of new nodes to already existing nodes that are geographically close. We analyze several topological properties for our model both analytically and by numerical simulations. The resulting network shows some important characteristics of real-life networks such as the small-world effect and a high clustering.Comment: 11 pages, 4 figure

L-Shape based Layout Fracturing for E-Beam Lithography

Layout fracturing is a fundamental step in mask data preparation and e-beam lithography (EBL) writing. To increase EBL throughput, recently a new L-shape writing strategy is proposed, which calls for new L-shape fracturing, versus the conventional rectangular fracturing. Meanwhile, during layout fracturing, one must minimize very small/narrow features, also called slivers, due to manufacturability concern. This paper addresses this new research problem of how to perform L-shaped fracturing with sliver minimization. We propose two novel algorithms. The first one, rectangular merging (RM), starts from a set of rectangular fractures and merges them optimally to form L-shape fracturing. The second algorithm, direct L-shape fracturing (DLF), directly and effectively fractures the input layouts into L-shapes with sliver minimization. The experimental results show that our algorithms are very effective

Distributed context discovering for predictive modeling

Click prediction has applications in various areas such as advertising, search and online sales. Usually user-intent information such as query terms and previous click history is used in click prediction. However, this information is not always available. For example, there are no queries from users on the webpages of content publishers, such as personal blogs. The available information for click prediction in this scenario are implicitly derived from users, such as visiting time and IP address. Thus, the existing approaches utilizing user-intent information may be inapplicable in this scenario; and the click prediction problem in this scenario remains unexplored to our knowledge. In addition, the challenges in handling skewed data streams also exist in prediction, since there is often a heavy traffic on webpages and few visitors click on them. In this thesis, we propose to use the pattern-based classification approach to tackle the click prediction problem. Attributes in webpage visits are combined by a pattern mining algorithm to enhance their power in prediction. To make the pattern-based classification handle skewed data streams, we adopt a sliding window to capture recent data, and an undersampling technique to handle the skewness. As a side problem raised by the pattern-based approach, mining patterns from large datasets is addressed by a distributed pattern sampling algorithm proposed by us. This algorithm shows its scalability in experiments. We validate our pattern-based approach in click prediction on a real-world dataset from a Dutch portal website. The experiments show our pattern-based approach can achieve an average AUC of 0.675 over a period of 36 days with a 5-day sized sliding window, which surpasses the baseline, a statically trained classification model without patterns by 0.002. Besides, the average weighted F-measure of our approach is 0.009 higher than the baseline. Therefore, our proposed approach can slightly improve classification performance; yet whether this improvement worth deployment in real scenarios remains a question. Click prediction has applications in various areas such as advertising, search and online sales. Usually user-intent information such as query terms and previous click history is used in click prediction. However, this information is not always available. For example, there are no queries from users on the webpages of content publishers, such as personal blogs. The available information for click prediction in this scenario are implicitly derived from users, such as visiting time and IP address. Thus, the existing approaches utilizing user-intent information may be inapplicable in this scenario; and the click prediction problem in this scenario remains unexplored to our knowledge. In addition, the challenges in handling skewed data streams also exist in prediction, since there is often a heavy traffic on webpages and few visitors click on them. In this thesis, we propose to use the pattern-based classification approach to tackle the click prediction problem. Attributes in webpage visits are combined by a pattern mining algorithm to enhance their power in prediction. To make the pattern-based classification handle skewed data streams, we adopt a sliding window to capture recent data, and an undersampling technique to handle the skewness. As a side problem raised by the pattern-based approach, mining patterns from large datasets is addressed by a distributed pattern sampling algorithm proposed by us. This algorithm shows its scalability in experiments. We validate our pattern-based approach in click prediction on a real-world dataset from a Dutch portal website. The experiments show our pattern-based approach can achieve an average AUC of 0.675 over a period of 36 days with a 5-day sized sliding window, which surpasses the baseline, a statically trained classification model without patterns by 0.002. Besides, the average weighted F-measure of our approach is 0.009 higher than the baseline. Therefore, our proposed approach can slightly improve classification performance; yet whether this improvement worth deployment in real scenarios remains a question

Methodology for standard cell compliance and detailed placement for triple patterning lithography

As the feature size of semiconductor process further scales to sub-16nm technology node, triple patterning lithography (TPL) has been regarded one of the most promising lithography candidates. M1 and contact layers, which are usually deployed within standard cells, are most critical and complex parts for modern digital designs. Traditional design flow that ignores TPL in early stages may limit the potential to resolve all the TPL conflicts. In this paper, we propose a coherent framework, including standard cell compliance and detailed placement to enable TPL friendly design. Considering TPL constraints during early design stages, such as standard cell compliance, improves the layout decomposability. With the pre-coloring solutions of standard cells, we present a TPL aware detailed placement, where the layout decomposition and placement can be resolved simultaneously. Our experimental results show that, with negligible impact on critical path delay, our framework can resolve the conflicts much more easily, compared with the traditional physical design flow and followed layout decomposition

E-BLOW: E-Beam Lithography Overlapping aware Stencil Planning for MCC System

Electron beam lithography (EBL) is a promising maskless solution for the technology beyond 14nm logic node. To overcome its throughput limitation, recently the traditional EBL system is extended into MCC system. %to further improve the throughput. In this paper, we present E-BLOW, a tool to solve the overlapping aware stencil planning (OSP) problems in MCC system. E-BLOW is integrated with several novel speedup techniques, i.e., successive relaxation, dynamic programming and KD-Tree based clustering, to achieve a good performance in terms of runtime and solution quality. Experimental results show that, compared with previous works, E-BLOW demonstrates better performance for both conventional EBL system and MCC system

High Dimensional Apollonian Networks

We propose a simple algorithm which produces high dimensional Apollonian networks with both small-world and scale-free characteristics. We derive analytical expressions for the degree distribution, the clustering coefficient and the diameter of the networks, which are determined by their dimension