Arun V. Sathanur

A Hierarchical Simulation Flow for Return-Loss Optimization of Microprocessor Package Vertical Interconnects

Design of package- and board-level interconnects utilizing full-wave electromagnetic solvers, is becoming increasingly important owing to increased frequencies of operation, miniaturization, and reduced time to market. Thus, parameterization, optimization, and statistical analysis tools are becoming an invaluable part of a designers armory. Leveraging a previously developed fast full-wave electromagnetic solver, this paper addresses the development of a framework for package interconnect design. Parametric sweeps are conducted to show the existence of optimal designs and to select the best routing strategies. Having applied the popular response surface methodology for optimization and having outlined its limitations for higher-dimensional problems, a general optimization scheme is proposed and illustrated on a differential package interconnect line. The proposed methodology features a dimensionality reduction scheme and a reusable, multidimensional look-up table preceding the global optimization phase, which is facilitated by a smooth interpolation scheme based on splines. The second phase features a custom local optimizer incorporating all the variables without any dimension reduction. This methodology has been applied to automated synthesis of a differential package line resulting in a significant improvement of the return loss performance. A statistical analysis methodology, based on utilizing the gradient, has been presented to arrive at the spread in the differential return loss, occurring due to manufacturing tolerances, around the designed response.

PHYSENSE: Scalable sociological interaction models for influence estimation on online social networks

The explosion in social media adoption has opened up new opportunities for next-generation personalized web and information exchange in big data scenarios. Making sense of the massive number of overlapping streams of information generated by hundreds of millions of users on large social networks requires novel analytics and scalable computational techniques. This paper introduces PHYSENSE, a scalable framework for influence computation and activity prediction on large online social networks (OSNs). Drawing inspiration from the Friedkin-Johnsen model for opinion change, PHYSENSE estimates and sets up sociological influence models to compute the diffusion of activity potential in the neighborhood of each of the nodes. PHYSENSE then scales these to significant parts of the entire OSN by propagating these activity potentials through an equivalent Helmholtz Greens function. Examples to show the enhanced quality of PHYSENSE in influence detection over popular existing methods based on variations of PageRank are presented. Additionally, for enhanced speedup, the community structures found in the social graphs along with low-rank updates are exploited in the acceleration of both the setup and the dynamic update phases of the influence computation.

Return loss optimization of the microprocessor package vertical interconnect

A geometry based parametric model of a differential high-speed line traversing a ten-layer microprocessor package is developed. This model is used to undertake a detailed study of the effect of the various geometrical parameters on the return loss performance of such a package. The forward problem is solved using a fast, full-wave electromagnetic (EM) solver. The effect of various types of routing in the intermediate layers is examined closely. A sensitivity analysis based on parametric sweeps is carried out to identify the key variables. A hierarchical response surface based optimization is carried out to arrive at an optimum structure. A global optimizer based on simulated annealing is harnessed to find the optimum of non-linear and, in general, non-convex functions. The optimized structure exhibits excellent return loss characteristics translating into higher channel bandwidth.

Simulating context-driven activity cascades in online social networks on the google exacycle platform

In this work a micro-scale generative model for simulating context-driven information cascades in online social networks is presented and analyzed. Activity cascades on online social networks are explained by the dynamic variation in the spectral radius of the sociologically derived local influence matrix. A stochastic discrete-event agent-based simulator working with synthesized graph topologies, that emulates peoples behavior on online social networks is used in conjunction with time-varying local models to generate macro-level activity cascades.

A partial homomorphic encryption scheme for secure design automation on public clouds

The massive compute power offered by public clouds such as Amazon Web Services EC2 provides for a new paradigm in cost-effective and highly scalable parallel deployment of Electronic Design Automation (EDA) tools. Even though the advantages are myriad, customers perceive an inherent security risk in exposing their IP to the cloud. In this work, we start by outlining the shortcomings of established encryption techniques for use in public machines. Using the example of electromagnetic simulation, we show how the IP (layout and technology) may be reverse-engineered from the Greens function matrix by utilizing the Multi Dimensional Scaling approach. We then propose encryption schemes to defeat such sophisticated hacking attempts using principles of homomorphic encryption, while enabling scalability and computational benefits of public clouds.

A Hierarchical Electromagnetic-Circuit Technique for Statistical Analysis of RF Circuits in the Spectral Domain

Spectral domain statistical analysis of RF circuits, combining a circuit simulator, which models the circuit part and a full-wave field solver, which models the passive elements, is presented in this paper. This paper first illustrates the importance of the knowledge of correlation information in accurately modeling the probability density functions (PDFs) of eventual objective functions using a simple transmission line paradigm. Next, this paper looks at the statistical study of on-chip RF passives using the spiral inductor as an example. It is shown that larger process variations necessitate modeling by means of a quadratic response surface to preserve accuracy. This results in nonindependent non-Gaussian nonclosed-form PDFs for the equivalent-circuit parameters of the passives. This paper then proposes a hierarchical technique to perform statistical analysis of RF circuits based on y-parameter representation for the circuit and the passive element parts. The proposed technique obviates the need for optimization steps to derive the equivalent-circuit parameters for the electromagnetic objects and the need to compute the correlation matrix between the circuit equivalent elements, while maintaining accuracy. The proposed approach is illustrated for the statistical analysis of an RF amplifier and its differential version operating at 15.78 GHz. PDFs of various quantities of interest are derived and yield measures are computed.

Statistical analysis of RF circuits using combined circuit simulator-full wave field solver approach

As technologies continue to shrink in size, modeling the effect of process variations on circuit performance is assuming profound significance. Process variations affect the on-chip performance of both active and passive components. This necessitates the inclusion of the effect of these variations on distributed interconnect structures in modeling overall circuit performance. In this work, first it is shown through field-solver simulations that larger process variations lead to non-Gaussian PDFs (probability density functions) for the circuit equivalent parameters of distributed passives. Next, a method for accurate statistical analysis of coupled circuit-EM (Electromagnetic) systems without computing the equivalent circuit parameters of EM-modeled objects is demonstrated. This method also obviates the need to generate random variables representing the equivalent circuit parameters, from distributions which are correlated, non-Gaussian and non-closed-form. The proposed approach relies on application of the response surface (RS) methodology to the y-parameters of both the circuit and the distributed structures independently and expressing the eventual performance measures through a suitable combination of the y-parameters. The eventual performance measures are expressed through a hierarchical approach in terms of the underlying Gaussian random variables representing the process parameters. A rapid response surface Monte Carlo (RSMC) analysis on these derived response surfaces furnishes the PDFs and can also be used to predict the yield based on different qualifying criteria and objective functions.

Active-passive co-synthesis of multi-GigaHertz radio frequency circuits with broadband parametric macromodels of on-chip passives

Synthesis of multi-GigaHertz radio frequency circuits brings together difficult challenges related to simulation, extraction and multidimensional space search. The standard approach of mapping all electromagnetic parasitics into parametric RLC models prior to synthesis is extremely restrictive especially when broadband and full-wave models with high accuracy are needed. In the presented approach, a two-stage macromodel that creates broadband, accurate parametric representations of passives, in particular spiral inductors, is developed. The broadband nature is captured through the Vector Fitting algorithm. The macromodels are implemented via efficient nonlinear, multidimensional regression using Relevance Vector Machine, and are coupled into circuit simulators through admittance parameters. Subsequently, optimization on both active and passive parameters are carried out simultaneously, thereby bypassing the ad hoc nature of two stage (actives and passives) approximate optimization. Two standard low-noise amplifier topologies are synthesized with tight performance constraints at center frequencies 5, 10 and 12 GigaHertz in order to demonstrate the frequency scalability of the methodology.

Accurate statistical analysis of a differential low noise amplifier using a combined SPICE-field solver approach

With continuing trends towards miniaturization of circuits and inclusion of multiple, complex functionalities on a single chip, the effect of process variations on circuit performance is assuming critical importance. In view of increasing frequency of operation, accurate variability analysis of RF/Microwave circuits would require modeling of the variability in the passive elements through a field solver. In this paper, a method for enabling accurate statistical analysis of a low noise amplifier and its differential version is proposed. The on-chip spiral inductors are modeled through an EM (Electromagnetic) solver, while the circuit part is modeled through SPICE. The proposed approach relies on application of the RS (Response Surface) methodology to the y-parameters of both the circuit and the inductors independently and expressing the eventual performance measures through a suitable combination of these y-parameters. The eventual performance measures are expressed through a hierarchical approach in terms of the underlying Gaussian random variables representing both the circuit and EM process parameters. An RSMC (Rapid Response Surface Monte Carlo) analysis on these derived response surfaces furnishes the PDFs and can also be used to predict the yield based on different qualifying criteria and objective functions. Several advantages of this method are outlined.

Social sensor analytics: Making sense of network models in social media

We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the month of June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify a signature of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We have made our time series and dynamic graph analytical code available via a GitHub repository 1 and our data are available upon request.