Fast Design Optimization Through Simple Kriging Metamodeling: A Sense Amplifier Case Study

Due to the increasing complexity of nanoscale CMOS circuits and systems integration, full SPICE simulations for silicon accurate results can have run times in the order of days or weeks. This paper presents a methodology that uses a simple Kriging metamodeling technique capable of modeling the correlation effects between parameters, and a simulated annealing algorithm for ultrafast design optimization. The proposed methodology is applied to a clamped bitline amplifier circuit, which shows promising results for increased accuracy in process-aware metamodeling techniques. The error of the metamodels is very small, which is generated in 10.5 min compared to the 72 h taken for an exhaustive simulation. The design optimization performed on the metamodels improves the precharge time of the circuit by 61.15%.

[1]  Saraju P. Mohanty,et al.  Design of Parasitic and Process-Variation Aware Nano-CMOS RF Circuits: A VCO Case Study , 2009, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[2]  E. Kougianos,et al.  A Comparative Study of Metamodels for Fast and Accurate Simulation of Nano-CMOS Circuits , 2012, IEEE Transactions on Semiconductor Manufacturing.

[3]  Saraju P. Mohanty,et al.  Kriging-Assisted Ultra-Fast Simulated-Annealing Optimization of a Clamped Bitline Sense Amplifier , 2012, 2012 25th International Conference on VLSI Design.

[4]  Barry L. Nelson,et al.  Stochastic kriging for simulation metamodeling , 2008, WSC 2008.

[5]  M. Kuhl,et al.  KRIGING METAMODELING IN DISCRETE-EVENT SIMULATION : AN OVERVIEW , 2005 .

[6]  Dan Wang,et al.  Kriging Model combined with latin hypercube sampling for surrogate modeling of analog integrated circuit performance , 2009, 2009 10th International Symposium on Quality Electronic Design.

[7]  Piet Demeester,et al.  A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design , 2010, J. Mach. Learn. Res..

[8]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[9]  Chen Ling,et al.  A metamodel-based optimization method for mobile ad hoc networks , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[10]  Saraju P. Mohanty,et al.  Towards robust nano-CMOS sense amplifier design: a dual-threshold versus dual-oxide perspective , 2011, GLSVLSI '11.

[11]  Richard C. Jaeger,et al.  A subnanosecond clamped-bit-line sense amplifier for 1T dynamic RAMs , 1991, 1991 International Symposium on VLSI Technology, Systems, and Applications - Proceedings of Technical Papers.

[12]  Shi-Yu Huang,et al.  A Resilient and Power-Efficient Automatic-Power-Down Sense Amplifier for SRAM Design , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[13]  Peng Li,et al.  Yield-aware analog integrated circuit optimization using geostatistics motivated performance modeling , 2007, 2007 IEEE/ACM International Conference on Computer-Aided Design.

[14]  G. Matheron.  Principles of geostatistics , 1963 .