Fogging and proximity effects (FPEs) are two major factors that cause inaccurate exposure and layout pattern distortions in e-beam lithography. In this article, we propose an analytical placement algorithm that considers both FPEs. We formulate the global placement problem as a separable minimization problem with linear constraints, where different objectives can be tackled one by one in an alternating fashion. Then, we propose a novel proximal group alternating direction method of multipliers (ADMM) to solve the separable minimization problem with two subproblems, where the first subproblem (associated with wirelength and density) is solved by the steepest descent method without line search, and the second one (associated with the FPEs) is handled by an analytical scheme. We prove the property of global convergence of the proximal group ADMM method. Finally, the FPEs-aware legalization and detailed placement are employed to legalize and improve the placement result. The experimental results show that our algorithm is effective and efficient for the addressed problem. Our algorithm achieved 5.7% smaller fogging variation, 6.8% lower proximity variation, and 5.4% lower runtime with a minor wirelength overhead compared with the state-of-the-art work.

The sparse representation-based classifier (SRC) has been developed and verified as having great potential for real-world face recognition. In this paper, we propose a discriminative projection and representation-based classification (DPRC) method to enhance the discriminant ability of the SRC. The proposed method first obtains a discriminative projection matrix not only maximizing the ratio of the distance within interclass over the distance within intraclass, but also minimizing the linear approximation error within intraclass. Then it maps the original data onto the discriminative space, and adopts an SRC method to obtain the final solution. An inexact augmented Lagrangian method of multiplier is proposed for finding the optimal representation vector in our framework, and a proximal alternating minimization method is adopted to the iteration subproblems of the proposed method. The proposed method is proven to have the subsequence convergence property. Experimental results on Yale, ORL, and AR face image databases demonstrate that, compared with some existing feature extraction methods based on the SRC, the proposed DPRC method is more efficient.

Global placement dominates the circuit placement process in its solution quality and efficiency. With increasing design complexity and various design constraints, it is desirable to develop an efficient, high-quality global placement algorithm for modern large-scale circuit designs. In this paper, we first analyze the properties of four nonlinear optimization methods (the quadratic penalty method, the Lagrange multiplier method, and two augmented Lagrangian methods) for global placement, and then develop a generalized augmented Lagrangian method to solve this problem. Our proposed method preserves the advantages of the quadratic penalty method and the augmented Lagrangian method, and provides a smooth progress from the quadratic penalty method to the augmented Lagrangian method. We prove that the proposed generalized augmented Lagrangian method is globally convergent for the original global placement problem, even with different constraints. Compared with the other four popular optimization methods, experimental results show that our method achieves the best quality and is robust for handling different objectives. In particular, our generalized augmented Lagrangian formulation is theoretically sound and can solve generic large-scale constrained nonlinear optimization problems, which are widely used in many fields.

Fogging and proximity effects are two major factors that cause inaccurate exposure and thus layout pattern distortions in e-beam lithography. In this paper, we propose the first analytical placement algorithm to consider both the fogging and proximity effects. We first formulate the global placement problem as a separable minimization problem with linear constraints, where different objectives can be tackled one by one in an alternating fashion. Then, we propose a novel proximal group alternating direction method of multipliers (ADMM) to solve the separable minimization problem with two subproblems, where the first subproblem (mainly associated with wirelength and density) is solved by a steepest descent method without line-search, and the second one (mainly associated with the fogging and proximity effects) is handled by an analytical scheme. We prove the property of global convergence of the proximal group ADMM method. Finally, legalization and detailed placement are used to legal and further improve the placement result. Experimental results show that our algorithm is effective and efficient for the addressed problem. Compared with the state-of-the-art work, our algorithm not only can achieve 13.4% smaller fogging variation and 21.4% lower proximity variation, but also has a 1.65× speedup.

The common objective of very large-scale integration (VLSI) placement problem is to minimize the total wirelength, which is calculated by the total half-perimeter wirelength (HPWL). Since the HPWL is not differentiable, various differentiable wirelength approximation functions have been proposed in analytical placement methods. In this paper, we reformulate the HPWL as an l1-norm model of the wirelength function, which is exact but nonsmooth. Based on the l1-norm wirelength model and exact calculation of overlapping areas between cells and bins, a nonsmooth optimization model is proposed for the VLSI global placement problem, and a subgradient method is proposed for solving the nonsmooth optimization problem. Moreover, local convergence of the subgradient method is proved under some suitable conditions. In addition, two enhanced techniques, i.e., an adaptive parameter to control the step size and a cautious strategy for increasing the penalty parameter, are also used in the nonsmooth optimization method. In order to make the placement method scalable, a multilevel framework is adopted. In the clustering stage, the best choice clustering algorithm is modified according to the l1-norm wirelength model to cluster the cells, and the nonsmooth optimization method is recursively used in the declustering stage. Comparisons of experimental results on the International Symposium on Physical Design (ISPD) 2005 and 2006 benchmarks show that the global placement method is promising.