Fast Stochastic Surrogate Modeling via Rational Polynomial Chaos Expansions and Principal Component Analysis

This paper introduces a fast stochastic surrogate modeling technique for the frequency-domain responses of linear and passive electrical and electromagnetic systems based on polynomial chaos expansion (PCE) and principal component analysis (PCA). A rational PCE model provides high accuracy, whereas the PCA allows compressing the model, leading to a reduced number of coefficients to estimate and thereby improving the overall training efficiency. Furthermore, the PCA compression is shown to provide additional accuracy improvements thanks to its intrinsic regularization properties. The effectiveness of the proposed method is illustrated by means of several application examples

Rational Polynomial Chaos Expansions for the Stochastic Macromodeling of Network Responses

This paper introduces rational polynomial chaos expansions for the stochastic modeling of the frequency-domain responses of linear electrical networks. The proposed method models stochastic network responses as a ratio of polynomial chaos expansions, rather than the standard single polynomial expansion. This approach is motivated by the fact that network responses are best represented by rational functions of both frequency and parameters. In particular, it is proven that the rational stochastic model is exact for lumped networks. The model coefficients are computed via an iterative re-weighted linear least-square regression. Several application examples, concerning both lumped and a distributed systems, illustrate and validate the advocated methodology

Sequence polymorphism from EST data in sugarcane: a fine analysis of 6-phosphogluconate dehydrogenase genes

This paper presents preliminary results demonstrating the use of the sugarcane expressed sequence tag (EST) database (SUCEST) to detect single nucleotide polymorphisms (SNPs) inside 6-phosphogluconate dehydrogenase genes (Pgds). Sixty-four Pgd-related EST sequences were identified and partitioned into two clear-cut sets of 14 and 50 ESTs, probably corresponding to two genes, A and B, respectively. Alignment of A sequences allowed the detection of a single SNP while alignment of B sequences permitted the detection of 39 reliable SNPs, 27 of which in the coding sequence of the gene. Thirty-eight SNPs were binucleotidic and a single one was trinucleotidic. Nine insertions/deletions from one to 72 base pairs long were also detected in the noncoding 3? and 5? sequences. The soundness and the consequences of those preliminary observations on sequence polymorphism in sugarcane are discussed.O presente estudo apresenta resultados preliminares demonstrando a utilização da base de dados de ESTs de cana-de-açúcar para detectar polimorfismo de base única (SNP para Single Nucleotide Polymorphism). Sessenta e quatro ESTs relacionados aos genes da 6-phosphogluconate deshydrogenases (Pgds) foram identificados e divididos em dois conjuntos bem delimitados, de 14 e 50 ESTs, correspondendo a dois genes, A e B. O alinhamento das seqüências do grupo A permitiu a detecção de um único SNP e o alinhamento das seqüências do grupo B permitiu a detecção de 39 SNP, incluindo 27 na região codificante do gene. Trinta e oito SNP foram bi-nucleotídicos e um único tri-nucleotídico. Nove inserções/supressões de um até 72 pares de base foram detectados nas regiões não-codificantes 3? ou 5?. A robustez e as conseqüências dessas observações preliminares são discutidas.16116

Noise compliant macromodel synthesis for RF and Mixed-Signal applications

This paper proposes a compact synthesis approach for reduced-order behavioral macromodels of linear circuit blocks for RF and Mixed-Signal design. The proposed approach revitalizes the classical synthesis of lumped linear and timeinvariant multiport networks by reactance extraction, which is here exploited to obtain reduced-order equivalent SPICE netlists that can be used in any type of system-level simulations, including transient and noise analysis. The effectiveness of proposed approach is demonstrated on a real design applicatio

DC-compliant small-signal macromodels of non-linear circuit blocks

This paper presents a novel strategy to improve the accuracy of macromodel-based approaches for fast Signal Integrity assessment for highly integrated Radio Frequency (RF) and Analog-Mixed-Signal (AMS) Systems on Chip (SoC). Specifically, we focus on small-signal representations of non-linear circuit blocks (CB) at prescribed DC operation points, which are approximated with low-order linearized macromodels to speed up the complex transient simulations required by common Signal-Integrity (SI) and Power Integrity (PI) verifications. In this paper, we propose a simple yet effective DC point correction strategy of the low-order macromodels, which enables their safe use in complete verification testbenches by ensuring exact biasing conditions for all circuit blocks. The numerical results show the effectiveness of the proposed model enhancement methodology, both in terms of accuracy and simulation time, when applied to several test cases of practical relevance for AMS and RF simulations