Presentation
Statistical analysis using AI-ML enabled SPICE solution to get tail samples for high linearity delta sigma converters
DescriptionContinuous Time Delta-Sigma Modulators are integral components in various RF and audio applications. They must achieve high linearity while maintaining efficiency in area usage and power consumption. Multi-bit quantization with dynamic element matching (DEM) techniques is generally employed to achieve linearity while limiting power consumption.
With the ever-increasing demand for linearity and stringent area requirements, it is desirable to use minimally sized DAC elements to reduce area and maximize the benefits of the DEM technique.
Traditional brute-force Monte Carlo methods for high sigma analysis are inefficient for obtaining valuable tail information of the Gaussian distribution, as they involve numerous simulations around the mean.
To address these challenges, a tool is needed that can accurately estimate yield and detect the worst-case tail samples with fewer simulations.
In this paper, we propose a ML enabled statistical analysis to estimate the worst-case tail samples which,
1. Successfully executed the impossible looking task of capturing the exact worst tail sample as would have been captured by standard brute-force monte-carlo (BFMC).
2. Achieved target spec of linearity (SFDR=90dB) with 4X reduction in DAC element area
3. 9X reduction in required number of samples to capture the worst tail sample as compared to BFMC
With the ever-increasing demand for linearity and stringent area requirements, it is desirable to use minimally sized DAC elements to reduce area and maximize the benefits of the DEM technique.
Traditional brute-force Monte Carlo methods for high sigma analysis are inefficient for obtaining valuable tail information of the Gaussian distribution, as they involve numerous simulations around the mean.
To address these challenges, a tool is needed that can accurately estimate yield and detect the worst-case tail samples with fewer simulations.
In this paper, we propose a ML enabled statistical analysis to estimate the worst-case tail samples which,
1. Successfully executed the impossible looking task of capturing the exact worst tail sample as would have been captured by standard brute-force monte-carlo (BFMC).
2. Achieved target spec of linearity (SFDR=90dB) with 4X reduction in DAC element area
3. 9X reduction in required number of samples to capture the worst tail sample as compared to BFMC
Event Type
Engineering Presentation
TimeMonday, June 2311:30am - 11:45am PDT
Location2010, Level 2
AI
IP