State of the Art and Recent Progress in Uncertainty Quantification for Electronic Systems (i.e. Variation-Aware or Stochastic Simulation)

Professor Luca Daniel
Electrical Engin. & Computer Science
Massachusetts Institute of Technology (MIT)
Cambridge, MA, USA
E-mail: luca@mit.edu
 

Abstract: On-chip and off chip fabrication process variations have become a major concern in today�s electronic systems design since they can significantly degrade systems� performance. Existing commercial circuit and MEMS simulators mostly rely on the well known Monte Carlo algorithm in order to predict and quantify such performance degradation. However during the last decade a large variety of more sophisticated and efficient alternative approaches have been proposed to accelerate such critical task. This talk will first review the state of the art of most modern uncertainty quantification techniques including intrusive and sampling-based ones. It will be shown in particular how parameterized model order reduction, and low-rank tensor based representations can be used to accelerate most uncertainty quantification tools and to handle the curse of dimensionality. Examples will be presented including amplifiers, mixers, voltage controlled oscillators with tunable micro-electro-mechanical capacitors and phase locked loops.

Short biography: Luca Daniel is an Associate Professor in the Electrical Engineering and Computer Science Department of the Massachusetts Institute of Technology (MIT). Prof. Daniel received the Ph.D. degree in Electrical Engineering from the University of California, Berkeley, in 2003. In 1998, he was with HP Research Labs, Palo Alto. In 2001, he was with Cadence Berkeley Labs.
Dr. Daniel research interests include development of integral equation solvers for very large complex systems, stochastic field solvers for large number of uncertainties, and automatic generation of parameterized stable compact models for linear and nonlinear dynamical systems. Applications of interest include simulation, modeling and optimization for mixed-signal/RF/mm-wave circuits, power electronics, MEMs, nanotechnologies, materials, MRI, and the human cardiovascular system.
Prof. Daniel has received the 1999 IEEE Trans. on Power Electronics best paper award; the 2003 best PhD thesis awards from both the Electrical Engineering and the Applied Math departments at UC Berkeley; the 2003 ACM Outstanding Ph.D. Dissertation Award in Electronic Design Automation; 5 best paper awards in international conferences, 8 additional nominations for best paper award; the 2009 IBM Corporation Faculty Award; and the 2010 IEEE Early Career Award in Electronic Design Automation..