Simulation-based sensitivity and worst-case analyses of automotive electronics

Simulation-based verification of electronic control units must face demands related to more functionality and less time to verify it. To ensure a reliable system, one must determine how the omnipresent, internal and external variations affect the target response, and find safe bounds for it. The main challenge is to optimally characterize a high number of sources of variation, with a reduced number of simulation runs. The paper conducts more efficient sensitivity and worst-case studies by applying concepts of Design of Experiments: screening to reduce the dimension of the verification space; sequential experiments for sensitivity analysis; gradient-based search for response bounds. The approach is evaluated on simulations of an airbag driver IC and compared with alternative methods.1

[1]  Richard Stone,et al.  Automotive Engineering Fundamentals , 2004 .

[2]  Rob A. Rutenbar,et al.  Statistical Blockade: A Novel Method for Very Fast Monte Carlo Simulation of Rare Circuit Events, and its Application , 2007, 2007 Design, Automation & Test in Europe Conference & Exhibition.

[3]  H. Theuerkauf,et al.  Robust identification of nonlinear dynamic systems using Design of Experiment , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[4]  Linda Trocine,et al.  An overview of newer, advanced screening methods for the initial phase in an experimental design , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[5]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[6]  Christoph Grimm,et al.  Modeling embedded systems using SystemC extensions , 2008 .

[7]  Navakanta Bhat,et al.  Response surface modeling of 100 nm CMOS process technology using design of experiment , 2004, 17th International Conference on VLSI Design. Proceedings..

[8]  Thorsten Grotker,et al.  System Design with SystemC , 2002 .

[9]  Frank Vahid,et al.  Soft-core Processor Customization using the Design of Experiments Paradigm , 2007, 2007 Design, Automation & Test in Europe Conference & Exhibition.

[10]  Iain Bate,et al.  Comparing design of experiments and evolutionary approaches to multi-objective optimisation of sensornet protocols , 2009, 2009 IEEE Congress on Evolutionary Computation.

[11]  Nicola Femia,et al.  True worst-case circuit tolerance analysis using genetic algorithms and affine arithmetic , 2000 .

[12]  Ying Chen,et al.  Microarchitecture-aware floorplanning using a statistical design of experiments approach , 2005, Proceedings. 42nd Design Automation Conference, 2005..