Yield-area optimizations of digital circuits using non-dominated sorting genetic algorithm (YOGA)

With shrinking technology, the timing variation of a digital circuit is becoming the most important factor while designing a functionally reliable circuit. Gate sizing has emerged as one of the efficient way to subside the yield deterioration due to manufacturing variations. In the past single-objective optimization techniques have been used to optimize the timing variation whereas on the other hand multi-objective optimization techniques can provide a more promising approach to design the circuit. We propose a new algorithm called YOGA, based on multi-objective optimization technique called non-dominated sorting genetic algorithm (NSGA). YOGA optimizes a circuit in multi domains and provides the user with Pareto-optimal set of solutions which are distributed all over the optimal design spectrum, giving users the flexibility to choose the best fitting solution for their requirements. YOGA overcomes the disadvantages of traditional optimization techniques, while even providing solutions in very stringent bounds

[1]  Kaushik Roy,et al.  Novel sizing algorithm for yield improvement under process variation in nanometer technology , 2004, Proceedings. 41st Design Automation Conference, 2004..

[2]  A. A. Ilumoka Optimal transistor sizing for CMOS VLSI circuits using modular artificial neural networks , 1997, Proceedings The Twenty-Ninth Southeastern Symposium on System Theory.

[3]  C. E. Clark The Greatest of a Finite Set of Random Variables , 1961 .

[4]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[5]  Chandramouli V. Kashyap,et al.  Block-based Static Timing Analysis with Uncertainty , 2003, ICCAD.

[6]  Xiaoyu Song,et al.  Improving the process-variation tolerance of digital circuits using gate sizing and statistical techniques , 2005, Design, Automation and Test in Europe.

[7]  Kalyanmoy Deb,et al.  Nonlinear goal programming using multi-objective genetic algorithms , 2001, J. Oper. Res. Soc..

[8]  K. Deb Non-linear Goal Programming Using Multi-Objective Genetic Algorithms , 1998 .

[9]  Hai Zhou,et al.  Timing yield estimation using statistical static timing analysis , 2005, 2005 IEEE International Symposium on Circuits and Systems.

[10]  Kwang-Ting Cheng,et al.  Fast statistical timing analysis by probabilistic event propagation , 2001, DAC '01.

[11]  Zhi-Quan Luo,et al.  Robust gate sizing by geometric programming , 2005, Proceedings. 42nd Design Automation Conference, 2005..

[12]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[13]  António Gaspar-Cunha,et al.  Use of Genetic Algorithms in Multicriteria Optimization to Solve Industrial Problems , 1997, ICGA.

[14]  John P. Hayes,et al.  Unveiling the ISCAS-85 Benchmarks: A Case Study in Reverse Engineering , 1999, IEEE Des. Test Comput..

[15]  Alberto L. Sangiovanni-Vincentelli,et al.  Synthesis for manufacturability: a sanity check , 2004, Proceedings Design, Automation and Test in Europe Conference and Exhibition.

[16]  Michael Orshansky,et al.  An efficient algorithm for statistical minimization of total power under timing yield constraints , 2005, Proceedings. 42nd Design Automation Conference, 2005..

[17]  Sarma B. K. Vrudhula,et al.  A methodology to improve timing yield in the presence of process variations , 2004, Proceedings. 41st Design Automation Conference, 2004..

[18]  Kalyanmoy Deb,et al.  MULTI-OBJECTIVE FUNCTION OPTIMIZATION USING NON-DOMINATED SORTING GENETIC ALGORITHMS , 1994 .

[19]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[20]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .