Michel Nakhla

Bio: Michel Nakhla is an academic researcher from Carleton University. The author has contributed to research in topics: Very-large-scale integration & Nonlinear system. The author has an hindex of 44, co-authored 340 publications receiving 7136 citations. Previous affiliations of Michel Nakhla include bell northern research.

Simulation of high-speed interconnects

Abstract: With the rapid developments in very large-scale integration (VLSI) technology, design and computer-aided design (CAD) techniques, at both the chip and package level, the operating frequencies are fast reaching the vicinity of gigahertz and switching times are getting to the subnanosecond levels. The ever increasing quest for high-speed applications is placing higher demands on interconnect performance and highlighted the previously negligible effects of interconnects such as ringing, signal delay, distortion, reflections, and crosstalk. In this review paper various high-speed interconnect effects are briefly discussed. In addition, recent advances in transmission line macromodeling techniques are presented. Also, simulation of high-speed interconnects using model-reduction-based algorithms is discussed in detail.

Analysis of interconnect networks using complex frequency hopping (CFH)

Abstract: With increasing miniaturization and operating speeds, loss of signal integrity due to physical interconnects represents a major performance limiting factor of chip-, board- or system-level design. Moment-matching techniques using Pade approximations have recently been applied to simulating modelled interconnect networks that include lossy coupled transmission lines and nonlinear terminations, giving a marked increase in efficiency over traditional simulation techniques. Nevertheless, moment-matching can be inaccurate in high-speed circuits due to critical properties of Pade approximations. Further, moment-generation for transmission line networks can be shown to have increasing numerical truncation error with higher order moments. These inaccuracies are reflected in both the frequency and transient response and there is no criterion for determining the limits of the error. In this paper, a multipoint moment-matching, or complex frequency hopping (CFH) technique is introduced which extracts accurate dominant poles of a linear subnetwork up to any predefined maximum frequency. The method generates a single transfer function for a large linear subnetwork and provides for a CPU/accuracy tradeoff. A new algorithm is also introduced for generating higher-order moments for transmission lines without incurring increasing truncation error. Several interconnect examples are considered which demonstrate the accuracy and efficiency in both the time and frequency domains of the new method. >

A neural network modeling approach to circuit optimization and statistical design

Abstract: The trend of using accurate models such as physics-based FET models, coupled with the demand for yield optimization results in a computationally challenging task. This paper presents a new approach to microwave circuit optimization and statistical design featuring neural network models at either device or circuit levels. At the device level, the neural network represents a physics-oriented FET model yet without the need to solve device physics equations repeatedly during optimization. At the circuit level, the neural network speeds up optimization by replacing repeated circuit simulations. This method is faster than direct optimization of original device and circuit models. Compared to existing polynomial or table look-up models used in analysis and optimization, the proposed approach has the capability to handle high-dimensional and highly nonlinear problems. >

Stability, Causality, and Passivity in Electrical Interconnect Models

Abstract: Modern packaging design requires extensive signal integrity simulations in order to assess the electrical performance of the system. The feasibility of such simulations is granted only when accurate and efficient models are available for all system parts and components having a significant influence on the signals. Unfortunately, model derivation is still a challenging task, despite the extensive research that has been devoted to this topic. In fact, it is a common experience that modeling or simulation tasks sometimes fail, often without a clear understanding of the main reason. This paper presents the fundamental properties of causality, stability, and passivity that electrical interconnect models must satisfy in order to be physically consistent. All basic definitions are reviewed in time domain, Laplace domain, and frequency domain, and all significant interrelations between these properties are outlined. This background material is used to interpret several common situations where either model derivation or model use in a computer-aided design environment fails dramatically. We show that the root cause for these difficulties can always be traced back to the lack of stability, causality, or passivity in the data providing the structure characterization and/or in the model itself.

Asymptotic Waveform Evaluation: And Moment Matching for Interconnect Analysis

Abstract: List of Figures. Preface. 1. Introduction. 2. Asymptotic Waveform Evaluation. 3. Transmission Lines. 4. Pade Approximations. 5. Accuracy Improvements. 6. Complex Frequency Hopping. 7. Nonlinear Analysis. 8. Sensitivity Analysis. 9. Other Applications. Appendix: Transmission Line Equations. Subject Index.

Differential Equations and Dynamical Systems

Abstract: Series Preface * Preface to the Third Edition * 1 Linear Systems * 2 Nonlinear Systems: Local Theory * 3 Nonlinear Systems: Global Theory * 4 Nonlinear Systems: Bifurcation Theory * References * Index

Nonlinear Time Series Analysis.

Abstract: : This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

Efficient linear circuit analysis by Pade approximation via the Lanczos process

Abstract: In this paper, we introduce PVL, an algorithm for computing the Pade approximation of Laplace-domain transfer functions of large linear networks via a Lanczos process. The PVL algorithm has significantly superior numerical stability, while retaining the same efficiency as algorithms that compute the Pade approximation directly through moment matching, such as AWE and its derivatives. As a consequence, it produces more accurate and higher-order approximations, and it renders unnecessary many of the heuristics that AWE and its derivatives had to employ. The algorithm also computes an error bound that permits to identify the true poles and zeros of the original network. We present results of numerical experiments with the PVL algorithm for several large examples. >