Nonlinear Black-Box Models of Digital Integrated Circuits via System Identification

This Thesis concerns the development of numerical macromodels of digi- tal Integrated Circuits input/output buffers. Such models are of paramount importance for the system-level simulation required for the assessment of Sig- nal Integrity and Electromagnetic Compatibility effects in high-performance electronic equipments via system-level simulations. In order to obtain accurate and efficient macromodels, we concentrate on the black-box modeling approach, exploiting system identification methods. The present study contributes to the systematic discussion of the IC mod- eling process, in order to obtain macromodels that can overcome strengths and limitations of the methodologies presented so far. The performances of different parametric representations, as Sigmoidal Basis Functions (SBF) ex- pansions, Echo State Networks (ESN) and Local Linear State-Space (LLSS) models are investigated. All representations have proven capabilities for the modeling of unknown nonlinear dynamic systems and are good candidates too be used for the modeling problem at hand. For each model representation, the most suitable estimation algorithm is considered and a systematic analy- sis is performed to highlight advantages and limitations. For this analysis, the modeling process is applied to a synthetic nonlinear device representative of IC ports, and designed to generate stiff responses. The tests carried out show that LLSS models provide the best overall performance for the modeling of digital devices, even with strong nonlinear dynamics. LLSS models can be estimated by means of an efficient algorithm providing a unique solution. Local stability of models is preconditioned and verified a posteriori. The effectiveness of the modeling process based on LLSS representations is verified by applying the proposed technique to the modeling of real devices involved in a realistic data communication link (an RF-to-Digital interface used in mobile phones). The obtained macromodels have been successfully used to predict both the functional signals and the power supply and ground fluctuations. Besides, they turn out to be very efficient, providing a signifi- cant simulation speed-up for the complete data lin