A new hybrid method for optimal circuit design using semi-definite programming

In this article a new method for yield optimization (design centring) is introduced. The method has a statistical-geometrical nature, hence it is called hybrid. The method exploits the semi-definite programming applications in approximating the feasible region with two bounding ellipsoids. These ellipsoids are obtained using a two phase algorithm. In the first phase, the minimum volume ellipsoid enclosing the feasible region is obtained. The largest ellipsoid that can be inscribed inside the feasible region is obtained in the second phase. The centres of these bounding ellipsoids are used as design centres. In the second phase, an additional polytopic region approximation is constructed. A comparison between the obtained region approximations is given. Saving in the number of circuit simulations needed for yield optimization is also considered. Practical examples are given to show the effectiveness of the new method.

[1]  Stephen P. Boyd,et al.  Determinant Maximization with Linear Matrix Inequality Constraints , 1998, SIAM J. Matrix Anal. Appl..

[2]  A. Rollett,et al.  The Monte Carlo Method , 2004 .

[3]  A. A. Rabie,et al.  Non-derivative design centering algorithm using trust region optimization and variance reduction , 2006 .

[4]  E. D. Klerk,et al.  Aspects of semidefinite programming : interior point algorithms and selected applications , 2002 .

[5]  Leszek J. Opalski,et al.  Design centering using an approximation to the constraint region , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[6]  Kim-Chuan Toh,et al.  Primal-Dual Path-Following Algorithms for Determinant Maximization Problems With Linear Matrix Inequalities , 1999, Comput. Optim. Appl..

[7]  Phillip E Allen,et al.  CMOS Analog Circuit Design , 1987 .

[8]  Kim-Chuan Toh,et al.  On the Implementation and Usage of SDPT3 – A Matlab Software Package for Semidefinite-Quadratic-Linear Programming, Version 4.0 , 2012 .

[9]  Abdel-Karim S.O. Hassan,et al.  Design centering and polyhedral region approximation via parallel-cuts ellipsoidal technique , 2004 .

[10]  Stephen P. Boyd,et al.  Applications of semidefinite programming , 1999 .

[11]  K. Singhal,et al.  Statistical design centering and tolerancing using parametric sampling , 1981 .

[12]  Anton van den Hengel,et al.  Semidefinite Programming , 2014, Computer Vision, A Reference Guide.

[13]  Helmut Graeb,et al.  Analog Design Centering and Sizing , 2007 .

[14]  G. Hachtel,et al.  Computationally efficient yield estimation procedures based on simplicial approximation , 1978 .

[15]  M. Todd A study of search directions in primal-dual interior-point methods for semidefinite programming , 1999 .

[16]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[17]  Masakazu Kojima,et al.  Exploiting sparsity in primal-dual interior-point methods for semidefinite programming , 1997, Math. Program..

[18]  Abdel-Karim S.O. Hassan Normed Distances and Their Applications in Optimal Circuit Design , 2003 .

[19]  Yin Zhang,et al.  On Numerical Solution of the Maximum Volume Ellipsoid Problem , 2003, SIAM J. Optim..

[20]  Monique Laurent,et al.  Semidefinite optimization , 2019, Graphs and Geometry.

[21]  Hany L. Abdel-Malek,et al.  The ellipsoidal technique for design centering and region approximation , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[22]  Michael L. Overton,et al.  Primal-Dual Interior-Point Methods for Semidefinite Programming: Convergence Rates, Stability and Numerical Results , 1998, SIAM J. Optim..

[23]  Stephen J. Wright Primal-Dual Interior-Point Methods , 1997, Other Titles in Applied Mathematics.

[24]  Timothy N. Trick,et al.  An Extrapolated Yield Approximation Technique for Use in Yield Maximization , 1984, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[25]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.