Layout Decomposition for Quadruple Patterning Lithography and Beyond

For next-generation technology nodes, multiple patterning lithography (MPL) has emerged as a key solution, e.g., triple patterning lithography (TPL) for 14/11nm, and quadruple patterning lithography (QPL) for sub-10nm. In this paper, we propose a generic and robust layout decomposition framework for QPL, which can be further extended to handle any general K-patterning lithography (K$>$4). Our framework is based on the semidefinite programming (SDP) formulation with novel coloring encoding. Meanwhile, we propose fast yet effective coloring assignment and achieve significant speedup. To our best knowledge, this is the first work on the general multiple patterning lithography layout decomposition.Comment: DAC'201

Search for single production of the vector-like top partner at the 14 TeV LHC

The new heavy vector-like top partner~($T$) is one of typical features of many new physics models beyond the standard model. In this paper we study the discovery potential of the LHC for the vector-like $T$-quark both in the leptonic $T\to bW$ and $T\to t_{\rm lep}Z_{\rm lep}$ (trilepton) channels at $\sqrt{s}= 14$ TeV in the single production mode. Our analysis is based on a simplified model including a $SU(2)_L$ singlet with charge $2/3$ with only two free parameters, namely the $TWb$ coupling parameter $g^{\ast}$ and the top partner mass $m_T$. The $2\sigma$ exclusion limits, $3\sigma$ evidence and the $5\sigma$ discovery reach in the parameter plane of $g^{\ast}-m_T$, are, respectively, obtained for some typical integrated luminosity at the 14 TeV LHC. Finally we analyze the projected sensitivity in terms of the production cross section times branching fraction for two decay channel.Comment: 15 pages, 10 figures, 2 tables. version in EPJ

An Evaluation of Text Classification Methods for Literary Study

This article presents an empirical evaluation of text classification methods in literary domain. This study compared the performance of two popular algorithms, naı¨ve Bayes and support vector machines (SVMs) in two literary text classification tasks: the eroticism classification of Dickinson’s poems and the sentimentalism classification of chapters in early American novels. The algorithms were also combined with three text pre-processing tools, namely stemming, stopword removal, and statistical feature selection, to study the impact of these tools on the classifiers’ performance in the literary setting. Existing studies outside the literary domain indicated that SVMs are generally better than naı¨ve Bayes classifiers. However, in this study SVMs were not all winners. Both algorithms achieved high accuracy in sentimental chapter classification, but the naı¨ve Bayes classifier outperformed the SVM classifier in erotic poem classification. Self-feature selection helped both algorithms improve their performance in both tasks. However, the two algorithms selected relevant features in different frequency ranges, and therefore captured different characteristics of the target classes. The evaluation results in this study also suggest that arbitrary featurereduction steps such as stemming and stopword removal should be taken very carefully. Some stopwords were highly discriminative features for Dickinson’s erotic poem classification. In sentimental chapter classification, stemming undermined subsequent feature selection by aggressively conflating and neutralizing discriminative features