Accelerated optical topography inspection using parameterized model order reduction

Lee, Jung Hoon; Vasilyev, Dmitry; Vithayathil, A.; Daniel, Luca; White, Jacob K.

doi:10.1109/mwsym.2005.1517065

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…This is a direct extension of the technique developed for the optimization of dynamical systems [2,8]. The moment matching technique (in the projection framework) was successfully applied to two-and multi-parameter models in [7,15] (also see [13] for application to inverse problems). Subspace generation in the multivariate moment matching approach is comparably simple.…”

Section: Introductionmentioning

confidence: 99%

On combining model reduction and Gauss–Newton algorithms for inverse partial differential equation problems

Druskin¹,

Zaslavsky²

2007

Inverse Problems

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We suggest an approach to speed up the Gauss–Newton solution of inverse partial differential equation problems by minimizing the number of forward problem calls. The acceleration is based on effective incorporation of the information from the previous iterations via a reduced-order model (ROM). It is designed with the help of Galerkin and pseudo-Galerkin methods for self-adjoint and complex symmetric problems respectively. The constructed ROM generates effective multivariate rational interpolation matching the forward solutions and the Jacobians from the previous iterations. Numerical examples for the inverse conductivity problem for the 3D Maxwell system show significant accelerations.

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Section: Introductionmentioning

confidence: 99%

On combining model reduction and Gauss–Newton algorithms for inverse partial differential equation problems

Druskin¹,

Zaslavsky²

2007

Inverse Problems

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Design Tools for Emerging Technologies

Johnson

Avniel

White

et al. 2006

2006 International Conference on Simulation of Semiconductor Processes and Devices

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The rapidly expanding diversity of technology available at the nanoscale is disrupting the existing transistorcentric microelectronics design paradigm, resulting in nearly decade-long delays between prototype demonstration and technology deployment. The key to reducing these innovationinhibiting delays is to develop algorithmic approaches that can start with first principles based descriptions of novel nanotechnology and rapidly and reliably synthesize manufacturable designs. Design tools are evolving this direction, with new extremely efficient yet customizable physical simulators, automatic paramterized low-order model extraction, and ever improving algorithms for robust optimization-new techniques that generate manufacturable designs by optimizing both system performance and robustness to manufacturing variations. In this paper we give a few examples of the coupling of such approaches, but mostly offer pointers to literature for researchers interested in contributed to this rapidly growing field of coupled simulation and robust optimization.

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