Limits of Conventional STA
STA, while very successful, has a number of limitations:
- Cannot easily handle within-die correlation, especially if spatial correlation is included.
- Needs many corners to handle all possible cases.
- If there are significant random variations, then in order to be conservative at all times, it is too pessimistic to result in competitive products.
- Changes to address various correlation problems, such as CPPR (Common Path Pessimism Removal) make the basic algorithm slower than linear time, or non-incremental, or both.
SSTA attacks these limitations more or less directly. First, SSTA uses sensitivities to find correlations among delays. Then it uses these correlations when computing how to add statistical distributions of delays.
Interestingly, there is no technical reason why determistic STA could not be enhanced to handle correlation and sensitivities, by keeping a vector of sensitivities with each value as SSTA does. Historically, this seemed like a big burden to add to STA, whereas it was clear it was needed for SSTA, so no-one complained. See some of the criticism of SSTA below where this alternative is proposed.
Read more about this topic: Statistical Static Timing Analysis
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