Solido Design Automation Introduces High-Sigma Monte Carlo Meta-simulator

High-Sigma Monte Carlo+ Package ~ Solido Design Automation

Solido Design Automation introduced their High-Sigma Monte Carlo (HSMC) meta-simulator. The Solido High-Sigma Monte Carlo meta-simulator solution features rapid analysis of yield/performance trade-offs for memory design. The High-Sigma Monte Carlo meta-simulator has already been successfully deployed at several companies because alternate methods were too slow, insufficiently inaccurate, and do not scale across the range of circuits memory designers need to analyze.

Solido’s HSMC meta-simulator achieves high-sigma memory verification in thousands rather than millions or billions of simulations. The solution analyzes the billions of Monte Carlo samples, and then focuses its SPICE simulation resources to find rare failures or validating the target yield. The simulator can run fast enough to facilitate both iterative design and verification within production timelines. A Solido HSMC 5 billion Monte Carlo sample run can take as little as 15 minutes.

HSMC is part of the Variation Designer product family. It provides SPICE-accurate information in the extreme tails of the high-sigma distribution, where defects are expected to occur and is applicable to production-scale high-sigma designs with hundreds of process variables. It interfaces to all the leading SPICE simulators used by memory designers and runs at the command-line, just like a single simulation, while supporting parallelization to hundreds of cores/machines and managing multiple simulations through LSF/SGE. It analyzes design sensitivities to variation, presenting design opportunities to shrink memory area, power and improve performance, as well as providing integrated results verification.

Meta-simulation background
A meta-simulator is like a single simulator. However, it can drive hundreds or thousands of simulations in parallel from traditional simulation engines. Meta-simulators offer a meta-level analysis that may utilize a large number of simulations, while keeping the user input to not much more than a netlist. The output is simple, numerical, and well-defined, just like the result of a simulation.

Meta-simulation has historically been limited to simplistic methods, such as running corners or running Monte Carlo. Today, meta-simulation techniques can be much more powerful, addressing designer challenges and speeding up different analyses types in precisely-targeted ways. For example, rather than running 100 or 10,000 PVT corners just to search for the worst cases, a Fast PVT meta-simulator would analyze all the PVT corners and intelligently simulate only the small subset requires to identify the worst-case corners with confidence.

Meta-simulation goes beyond distributed processing; it also adds efficiency to high-value analysis capabilities such as: fast PVT analysis; fast extraction of statistical corners; and fast sensitivity analysis. An ideal meta-simulator for memory design measures yield-performance tradeoffs out to 5 and 6-sigma, with the same accuracy as millions or billions of Monte Carlo simulations, but with 100x+ fewer simulations.

More info: Solido Design Automation Inc.