- Specifies process variation models and aging models together
- Stress simulation runs 1X on nominal and re-used for all iterations
- Looks at the results and compares how process variations impact aging
- Flexibility to use proprietary calculations for self-heating along with aging analysis
- Supports layout dependent effect (LDE) calculations and user-defined degradation models
- BTI recovery effect included
- Models incorporate HCI degradation saturation effects
Designers must find a way to accurately predict the effect of stress over the lifetime of a device, otherwise device wear-out can result in early end-of-life failure. Designers have had the enormous challenge of accounting for each source of device degradation in isolation, then using reliability analysis to estimate device degradation due to electrical stress followed by estimating the reduced lifetime based on estimated die temperature and the effect of process variation.
The Cadence® Legato™ Reliability Solution is the industry’s first solution to provide a holistic approach to reliability analysis. The solution unifies advanced aging analyses, so designers can include all the sources of device degradation in one place for their analysis including traditional electrical stress-based aging analysis, predictive models of aging effects, realistic stress conditions based on mission profiles, accelerated aging based on device operating temperature instead of the using the ambient temperature, and direct calculation of the process variation on aging instead of using estimated derating.
Traditional aging models developed for legacy nodes and planar CMOS transistors are unable to analyze new technologies like advanced nodes and FinFET transistors. The Legato Reliability Solution is a one-stop-shop solution that provides old and new device aging models based on the most recent research in device physics to better predict device degradation due to Hot Carrier Injection (HCI) and Bias Temperature Instability (BTI). With the Legato Reliability Solution, designers are now able to better predict advanced aging analysis over the operating lifetime of their designs.