Presentation
AI-ML meets SPICE to achieve 6-sigma Accuracy: A Revolution in Statistical Analysis
DescriptionA bandgap reference provides a stable voltage output that is relatively insensitive to temperature changes. This stability is crucial for maintaining consistent performance in radar systems. An increase in temperature drift of the bandgap output to 100 ppm/°C can significantly impact the performance of radar transmitters and receivers. It can lead to frequency instability, reduced power amplifier efficiency, signal processing errors, increased noise, and higher calibration requirements.
Ensuring minimal temperature drift in the bandgap reference is essential for maintaining the accuracy, reliability, and efficiency of radar systems. For this high sigma(4.2 sigma) montecarlo analysis is need to study the statistical variation. The standard monte-carlo flow involves lots of unnecessary simulations around the mean to get to the worst tail information of the Gaussian distribution.
In this paper, we are proposing an AI/ML enables Statistical solution flow (Spectre-FMC) to accurately detect the worst-case tail samples with fewer simulations and with below advantages.
• Turn-around time is reduced by 36X for 4.2 sigma accuracy
• Significant reduction in required number of samples from 1million to just 2600 runs to achieve
4.2 sigma accuracy makes this solution a highly sustainable solution
• Seamless integration in Virtuoso-ADE flow and easy setup reduces the learning curve for
designers
Ensuring minimal temperature drift in the bandgap reference is essential for maintaining the accuracy, reliability, and efficiency of radar systems. For this high sigma(4.2 sigma) montecarlo analysis is need to study the statistical variation. The standard monte-carlo flow involves lots of unnecessary simulations around the mean to get to the worst tail information of the Gaussian distribution.
In this paper, we are proposing an AI/ML enables Statistical solution flow (Spectre-FMC) to accurately detect the worst-case tail samples with fewer simulations and with below advantages.
• Turn-around time is reduced by 36X for 4.2 sigma accuracy
• Significant reduction in required number of samples from 1million to just 2600 runs to achieve
4.2 sigma accuracy makes this solution a highly sustainable solution
• Seamless integration in Virtuoso-ADE flow and easy setup reduces the learning curve for
designers
Event Type
Engineering Poster
TimeTuesday, June 245:00pm - 6:00pm PDT
LocationEngineering Posters, Level 2 Exhibit Hall