REscope: High-dimensional statistical circuit simulation towards full failure region coverage

Statistical circuit simulation is exhibiting increasing importance for circuit design under process variations. Existing approaches cannot efficiently analyze the failure probability for circuits with a large number of variation, nor handle problems with multiple disjoint failure regions. The proposed rare event microscope (REscope) first reduces the problem dimension by pruning the parameters with little contribution to circuit failure. Furthermore, we applied a nonlinear classifier which is capable of identifying multiple disjoint failure regions. In REscope, only likely-to-fail samples are simulated then matched to a generalized pareto distribution. On a 108-dimension charge pump circuit in PLL design, REscope outperforms the importance sampling and achieves more than 2 orders of magnitude speedup compared to Monte Carlo. Moreover, it accurately estimates failure rate, while the importance sampling totally fails because failure regions are not correctly captured.

[1]  Wei Wu,et al.  FPGA Accelerated Parallel Sparse Matrix Factorization for Circuit Simulations , 2011, ARC.

[2]  Rob A. Rutenbar,et al.  Statistical Blockade: Very Fast Statistical Simulation and Modeling of Rare Circuit Events and Its Application to Memory Design , 2009, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[3]  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).

[4]  J. Hosking,et al.  Parameter and quantile estimation for the generalized pareto distribution , 1987 .

[5]  Roland W. Freund,et al.  Efficient linear circuit analysis by Pade´ approximation via the Lanczos process , 1994, EURO-DAC '94.

[6]  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).

[7]  Cheol-Eui Lee Secondary Electron Generation in Electron-beam-irradiated Solids: Resolution Limits to Nanolithography , 2009 .

[8]  Wei Wu,et al.  An EScheduler-Based Data Dependence Analysis and Task Scheduling for Parallel Circuit Simulation , 2011, IEEE Transactions on Circuits and Systems II: Express Briefs.

[9]  P. Lugli,et al.  The Monte Carlo Method for Semiconductor Device Simulation , 1990 .

[10]  Rob A. Rutenbar,et al.  Statistical Blockade: A Novel Method for Very Fast Monte Carlo Simulation of Rare Circuit Events, and its Application , 2007, 2007 Design, Automation & Test in Europe Conference & Exhibition.

[11]  Hao Yu,et al.  Stochastic analog circuit behavior modeling by point estimation method , 2011, ISPD '11.

[12]  Lara Dolecek,et al.  Breaking the simulation barrier: SRAM evaluation through norm minimization , 2008, 2008 IEEE/ACM International Conference on Computer-Aided Design.

[13]  Rob A. Rutenbar,et al.  Recursive Statistical Blockade: An Enhanced Technique for Rare Event Simulation with Application to SRAM Circuit Design , 2008, 21st International Conference on VLSI Design (VLSID 2008).

[14]  J. Hosking Maximum‐Likelihood Estimation of the Parameters of the Generalized Extreme‐Value Distribution , 1985 .

[15]  J. R. Wallis,et al.  Estimation of the generalized extreme-value distribution by the method of probability-weighted moments , 1985 .

[16]  Lawrence T. Pileggi,et al.  Asymptotic Probability Extraction for Nonnormal Performance Distributions , 2007, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[17]  Wei Wu,et al.  Exploiting Parallelism by Data Dependency Elimination: A Case Study of Circuit Simulation Algorithms , 2013, IEEE Design & Test.

[18]  Wei Wu,et al.  Stochastic behavioral modeling of analog/mixed-signal circuits by maximizing entropy , 2013, International Symposium on Quality Electronic Design (ISQED).

[19]  Marko Robnik-Sikonja,et al.  Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF , 2004, Applied Intelligence.

[20]  Wei Wu,et al.  A fast and provably bounded failure analysis of memory circuits in high dimensions , 2014, 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC).

[21]  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.

[22]  M. Nakhla,et al.  Asymptotic Waveform Evaluation: And Moment Matching for Interconnect Analysis , 1993 .