SIM-DSP: A DSP-Enhanced CAD Platform for Signal Integrity Macromodeling and Simulation

Macromodeling-Simulation process for signal integrity verifications has become necessary for the high speed circuit system design. This paper aims to introduce a “VLSI Signal Integrity Macromodeling and Simulation via Digital Signal Processing Techniques” framework (known as SIM-DSP framework), which applies digital signal processing techniques to facilitate the SI verification process in the pre-layout design phase. Core identification modules and peripheral (pre-/post-)processing modules have been developed and assembled to form a verification flow. In particular, a single-step discrete cosine transform truncation (DCTT) module has been developed for modeling-simulation process. In DCTT, the response modeling problem is classified as a signal compression problem, wherein the system response can be represented by a truncated set of non-pole based DCT bases, and error can be analyzed through Parseval’s theorem. Practical examples are given to show the applicability of our proposed framework

Scalable macromodelling methodology for the efficient design of microwave filters

The complexity of the design of microwave filters increases steadily over the years. General design techniques available in literature yield relatively good initial designs, but electromagnetic (EM) optimisation is often needed to meet the specifications. Although interesting optimisation strategies exist, they depend on computationally expensive EM simulations. This makes the optimisation process time consuming. Moreover, brute force optimisation does not provide physical insights into the design and it is only applicable to one set of specifications. If the specifications change, the design and optimisation process must be redone. The authors propose a scalable macromodel-based design approach to overcome this. Scalable macromodels can be generated in an automated way. So far the inclusion of scalable macromodels in the design cycle of microwave filters has not been studied. In this study, it is shown that scalable macromodels can be included in the design cycle of microwave filters and re-used in multiple design scenarios at low computational cost. Guidelines to properly generate and use scalable macromodels in a filter design context are given. The approach is illustrated on a state-of-the-art microstrip dual-band bandpass filter with closely spaced pass bands and a complex geometrical structure. The results confirm that scalable macromodels are proper design tools and a valuable alternative to a computationally expensive EM simulator-based design flow

Efficient design optimization of complex electromagnetic systems using parametric macromodeling techniques

We propose a new parametric macromodeling technique for complex electromagnetic systems described by scattering parameters, which are parameterized by multiple design variables such as layout or substrate feature. The proposed technique is based on an efficient and reliable combination of rational identification, a procedure to find scaling and frequency shifting system coefficients, and positive interpolation schemes. Parametric macromodels can be used for efficient and accurate design space exploration and optimization. A design optimization example for a complex electromagnetic system is used to validate the proposed parametric macromodeling technique in a practical design process flow

Guaranteed passive parameterized admittance-based macromodeling

We propose a novel parametric macromodeling technique for admittance and impedance input-output representations parameterized by design variables such as geometrical layout or substrate features. It is able to build accurate multivariate macromodels that are stable and passive in the entire design space. An efficient combination of rational identification and interpolation schemes based on a class of positive interpolation operators, ensures overall stability and passivity of the parametric macromodel. Numerical examples validate the proposed approach on practical application cases

Frequency- and time-domain stochastic analysis of lossy and dispersive interconnects in a SPICE-like environment

This paper presents an improvement of the state-of-the-art polynomial chaos (PC) modeling of high-speed interconnects with parameter uncertainties via SPICE-like tools. While the previous model, due to its mathematical formulation, was limited to lossless lines, the introduction of modified classes of polynomials yields a formulation that allows to account for lossess and dispersion as well. Thanks to this, the new implementation can also take full advantage of the combination of the PC technique with macromodels that accurately describe the interconnect properties. An application example, i.e. the stochastic analysis of an on-chip line, validates and demonstrates the improved method

M[pi]log, Macromodeling via parametric identification of logic gates

This paper addresses the development of computational models of digital integrated circuit input and output buffers via the identification of nonlinear parametric models. The obtained models run in standard circuit simulation environments, offer improved accuracy and good numerical efficiency, and do not disclose information on the structure of the modeled devices. The paper reviews the basics of the parametric identification approach and illustrates its most recent extensions to handle temperature and supply voltage variations as well as power supply ports and tristate devices

Non intrusive polynomial chaos-based stochastic macromodeling of multiport systems

We present a novel technique to efficiently perform the variability analysis of electromagnetic systems. The proposed method calculates a Polynomial Chaos-based macromodel of the system transfer function that includes its statistical properties. The combination of a non-intrusive Polynomial Chaos approach with the Vector Fitting algorithm allows to describe the system variability features with accuracy and efficiency. The results of the variability analysis performed with the proposed method are verified by means of comparison with respect to the standard Monte Carlo analysis

Behavioral Modelling of Digital Devices Via Composite Local-Linear State-Space Relations

This paper addresses the generation of accurate and efficient behavioral models of digital ICs. The proposed approach is based on the approximation of the device port characteristics by means of composite local linear state-space relations whose parameters can effectively be estimated from device port transient responses via well-established system identification techniques. The proposedmodels have been proven to overcome some inherent limitations of the state-of-the-art models used so far, and they can effectively be implemented in any commercial tool as Simulation Program with Integrated Circuit Emphasis (SPICE) subcircuits or VHDL-AMS hardware descriptions. A systematic study of the performances of the proposed state-space models is carried out on a synthetic test device. The effectiveness of the proposed approach has been demonstrated on a real application problem involving commercial devices and a data link of a mobile phon