Variability-Aware Parametric Yield Estimation for Analog/Mixed-Signal Circuits: Concepts, Algorithms, and Challenges

Accurate yield estimation is always an important director of design. For analog/mixed signal circuits, the dominant yield loss mechanisms are parametric in nature. This paper provides an informative discussion of varied approaches to parametric yield estimation, including recently developed methods that provide a highly accurate and fast alternative to Monte Carlo methods for some types of analysis.

[1]  Harald Niederreiter,et al.  Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.

[2]  Kurt Antreich,et al.  The generalized boundary curve — a common method for automatic nominal design centering of analog circuits , 2000, DATE '00.

[3]  H. Graeb,et al.  The generalized boundary curve-a common method for automatic nominal design and design centering of analog circuits , 2000, Proceedings Design, Automation and Test in Europe Conference and Exhibition 2000 (Cat. No. PR00537).

[4]  Georges G. E. Gielen,et al.  Analysis of simulation-driven numerical performance modeling techniques for application to analog circuit optimization , 2005, 2005 IEEE International Symposium on Circuits and Systems.

[5]  Rajiv V. Joshi,et al.  Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events , 2006, 2006 43rd ACM/IEEE Design Automation Conference.

[6]  Rob A. Rutenbar,et al.  From Finance to Flip Flops: A Study of Fast Quasi-Monte Carlo Methods from Computational Finance Applied to Statistical Circuit Analysis , 2007, 8th International Symposium on Quality Electronic Design (ISQED'07).

[7]  Amit Mehrotra,et al.  Parameter Finding Methods for Oscillators with a Specified Oscillation Frequency , 2007, 2007 44th ACM/IEEE Design Automation Conference.

[8]  Jaijeet S. Roychowdhury,et al.  An efficient, fully nonlinear, variability-aware non-monte-carlo yield estimation procedure with applications to SRAM cells and ring oscillators , 2008, 2008 Asia and South Pacific Design Automation Conference.

[9]  Ronald L. Iman.  Latin Hypercube Sampling , 2008 .

[10]  Umut Arslan,et al.  Efficient statistical analysis of read timing failures in SRAM circuits , 2009, 2009 10th International Symposium on Quality Electronic Design.

[11]  Lara Dolecek,et al.  Loop flattening & spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis , 2010, 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010).

[12]  Hiroyuki Ochi,et al.  Sequential importance sampling for low-probability and high-dimensional SRAM yield analysis , 2010, 2010 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[13]  Yiyu Shi,et al.  QuickYield: An efficient global-search based parametric yield estimation with performance constraints , 2010, Design Automation Conference.