Pseudo Expected Improvement Based-Optimization for CMOS Analog Circuit Design

In this paper, we consider the use of a new parallel efficient global optimization algorithm based on the use of the pseudo expected improvement (PEI) criterion, for the optimal design of analog circuits. A comparison with the conventional efficient global optimization algorithm (EGO) is presented. We show, via two analog circuit designs that the proposed approach gives the same optimal circuit sizing but within reduced computing time.

[1]  Samir Ben Salem,et al.  A high performances CMOS CCII and high frequency applications , 2006 .

[2]  Esteban Tlelo-Cuautle,et al.  Richardson extrapolation-based sensitivity analysis in the multi-objective optimization of analog circuits , 2013, Appl. Math. Comput..

[3]  E. Roca,et al.  Generation of surrogate models of Pareto-optimal performance trade-offs of planar inductors , 2014 .

[4]  Nuno Horta,et al.  Analog circuits optimization based on evolutionary computation techniques , 2010, Integr..

[5]  Yuansheng Cheng,et al.  Balancing global and local search in parallel efficient global optimization algorithms , 2017, J. Glob. Optim..

[6]  Nuno Horta,et al.  Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques , 2010, Studies in Computational Intelligence.

[7]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[8]  Zhong-Hua Han,et al.  Surrogate-Based Optimization , 2012, Engineering Design Optimization.

[9]  Rafael Castro-Lopez,et al.  Analog/RF and Mixed-Signal Circuit Systematic Design , 2013 .

[10]  Andrew R. Conn,et al.  Optimization of custom MOS circuits by transistor sizing , 1996, ICCAD 1996.

[11]  Ziyan Ren,et al.  Comparative Study on Kriging Surrogate Models for Metaheuristic Optimization of Multidimensional Electromagnetic Problems , 2015, IEEE Transactions on Magnetics.

[12]  Rammohan Mallipeddi,et al.  An evolving surrogate model-based differential evolution algorithm , 2015, Appl. Soft Comput..

[13]  Esteban Tlelo-Cuautle,et al.  Expected Improvement-Based Optimization Approach for the Optimal Sizing of a CMOS Operational Transconductance Amplifier , 2018, 2018 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD).

[14]  Luiz Lebensztajn,et al.  Surrogate Modeling and Two-Level Infill Criteria Applied to Electromagnetic Device Optimization , 2015, IEEE Transactions on Magnetics.

[15]  Joseph Morlier,et al.  Efficient global optimization for high-dimensional constrained problems by using the Kriging models combined with the partial least squares method , 2018 .

[16]  Ernesto Benini,et al.  A Kriging-assisted multiobjective evolutionary algorithm , 2017, Appl. Soft Comput..

[17]  Nader Ale Ebrahim,et al.  Recent developments in metamodel based robust black-box simulation optimization: An overview , 2019, Decision Science Letters.

[18]  Yuansheng Cheng,et al.  Pseudo expected improvement criterion for parallel EGO algorithm , 2017, J. Glob. Optim..

[19]  Francisco V. Fernández,et al.  A two-step surrogate modeling strategy for single-objective and multi-objective optimization of radiofrequency circuits , 2019, Soft Comput..