Exploiting implicit information from data for linear macromodeling

In macromodeling, data points of sampled structure responses are always matched to construct linear macromodels for transient simulations of packaging structures. However, implicit information from sampled data has not been exploited comprehensively to facilitate the identification process. In this paper, we exploit implicit information from the sampled data for linear marcomodeling. First, in order to include complementary data for a more informative identification, we propose a discrete-time domain identification framework for frequency-/time-/hybrid-domain macromodeling. Second, we introduce pre-/post-processing techniques (e.g., P-norm identification criterion and warped frequency-/hybrid-domain identification) to interpret implicit information for configurations of identifications. Various examples from chip-level to board-level are used to demonstrate the performance of the proposed framework. © 2013 IEEE.published_or_final_versio

Low passband sensitivity digital filters: A generalized viewpoint and synthesis procedures

The concepts of losslessness and maximum available power are basic to the low-sensitivity properties of doubly terminated lossless networks of the continuous-time domain. Based on similar concepts, we develop a new theory for low-sensitivity discrete-time filter structures. The mathematical setup for the development is the bounded-real property of transfer functions and matrices. Starting from this property, we derive procedures for the synthesis of any stable digital filter transfer function by means of a low-sensitivity structure. Most of the structures generated by this approach are interconnections of a basic building block called digital "two-pair," and each two-pair is characterized by a lossless bounded-real (LBR) transfer matrix. The theory and synthesis procedures also cover special cases such as wave digital filters, which are derived from continuous-time networks, and digital lattice structures, which are closely related to unit elements of distributed network theory

Efficient resonance computations for Helmholtz problems based on a Dirichlet-to-Neumann map

We present an efficient procedure for computing resonances and resonant modes of Helmholtz problems posed in exterior domains. The problem is formulated as a nonlinear eigenvalue problem (NEP), where the nonlinearity arises from the use of a Dirichlet-to-Neumann map, which accounts for modeling unbounded domains. We consider a variational formulation and show that the spectrum consists of isolated eigenvalues of finite multiplicity that only can accumulate at infinity. The proposed method is based on a high order finite element discretization combined with a specialization of the Tensor Infinite Arnoldi method. Using Toeplitz matrices, we show how to specialize this method to our specific structure. In particular we introduce a pole cancellation technique in order to increase the radius of convergence for computation of eigenvalues that lie close to the poles of the matrix-valued function. The solution scheme can be applied to multiple resonators with a varying refractive index that is not necessarily piecewise constant. We present two test cases to show stability, performance and numerical accuracy of the method. In particular the use of a high order finite element discretization together with TIAR results in an efficient and reliable method to compute resonances

Interleavers

The chapter describes principles, analysis, design, properties, and implementations of optical frequency (or wavelength) interleavers. The emphasis is on finite impulse response devices based on cascaded Mach-Zehnder-type filter elements with carefully designed coupling ratios, the so-called resonant couplers. Another important class that is discussed is the infinite impulse response type, based on e.g. Fabry-Perot, Gires-Tournois, or ring resonators