1,302 research outputs found
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
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
LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
Recommendation models mainly deal with categorical variables, such as
user/item ID and attributes. Besides the high-cardinality issue, the
interactions among such categorical variables are usually long-tailed, with the
head made up of highly frequent values and a long tail of rare ones. This
phenomenon results in the data sparsity issue, making it essential to
regularize the models to ensure generalization. The common practice is to
employ grid search to manually tune regularization hyperparameters based on the
validation data. However, it requires non-trivial efforts and large computation
resources to search the whole candidate space; even so, it may not lead to the
optimal choice, for which different parameters should have different
regularization strengths. In this paper, we propose a hyperparameter
optimization method, LambdaOpt, which automatically and adaptively enforces
regularization during training. Specifically, it updates the regularization
coefficients based on the performance of validation data. With LambdaOpt, the
notorious tuning of regularization hyperparameters can be avoided; more
importantly, it allows fine-grained regularization (i.e. each parameter can
have an individualized regularization coefficient), leading to better
generalized models. We show how to employ LambdaOpt on matrix factorization, a
classical model that is representative of a large family of recommender models.
Extensive experiments on two public benchmarks demonstrate the superiority of
our method in boosting the performance of top-K recommendation.Comment: Accepted by KDD 201
A Novel Serpin with Antithrombin-Like Activity in Branchiostoma japonicum: Implications for the Presence of a Primitive Coagulation System
Serine protease inhibitors, or serpins, are a group of widely distributed proteins with similar structures that use conformational change to inhibit proteases. Antithrombin (AT) is a member of the serine protease inhibitor superfamily and a major coagulation inhibitor in all vertebrates, but its evolutionary origin remains elusive. In this study we isolated for the first time a cDNA encoding an antithrombin homolog, BjATl, from the protochordate Branchiostoma japonicum. The deduced protein BjATl consisted of 338 amino acids sharing 36.7% to 41.1% identity to known vertebrate ATs. BjATl contains a potential N-linked glycosylation site, two potential heparin binding sites and the reactive center loop with the absolutely conserved sequence Gly-Arg-Ser; all of these are features characteristic of ATs. All three phylogenetic trees constructed using Neighbor-Joining, Maximum-Likelihood and Bayesian-Inference methods also placed BjATl together with ATs. Moreover, BjATl expressed in yeast cells was able to inhibit bovine thrombin activity by forming a SDS-stable BjATl-thrombin complex. It also displays a concentration-dependent inhibition of thrombin that is accelerated by heparin. Furthermore, BjATl was predominantly expressed in the hepatic caecum and hind-gut, agreeing with the expression pattern of AT in mammalian species. All these data clearly demonstrate that BjATl is an ortholog of vertebrate ATs, suggesting that a primitive coagulation system emerged in the protochordate
State Estimation of Wireless Sensor Networks in the Presence of Data Packet Drops and Non-Gaussian Noise
Distributed Kalman filter approaches based on the maximum correntropy
criterion have recently demonstrated superior state estimation performance to
that of conventional distributed Kalman filters for wireless sensor networks in
the presence of non-Gaussian impulsive noise. However, these algorithms
currently fail to take account of data packet drops. The present work addresses
this issue by proposing a distributed maximum correntropy Kalman filter that
accounts for data packet drops (i.e., the DMCKF-DPD algorithm). The
effectiveness and feasibility of the algorithm are verified by simulations
conducted in a wireless sensor network with intermittent observations due to
data packet drops under a non-Gaussian noise environment. Moreover, the
computational complexity of the DMCKF-DPD algorithm is demonstrated to be
moderate compared with that of a conventional distributed Kalman filter, and we
provide a sufficient condition to ensure the convergence of the proposed
algorithm
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