Presentation
Using AI to validate standard cell Liberty IP riddled with sparse and disparate data
DescriptionValidation of Liberty (.lib) files received from external sources can be challenging and require significant resources. Several factors contribute to discrepancies in the accuracy of .lib files, such as the characterization settings used, the extracted netlist, SPICE models, the versions of simulators or tools employed, and the margins incorporated into the .lib file modeling process. Many of these factors may not be visible to end-users which adds complexity.
The proposed methodology enables users to plug the missing data for a dataset that is starved of it, thereby providing more clarity when comparing with the golden dataset. It leverages AI to allow end-users to overcome the challenge of limited information on how the IP was created. Using this, potential issues with external .lib files where incorrect process models were utilized during the characterization process were identified. By detecting this discrepancy much earlier in the overall development workflow, corrective actions were taken based on the deviations that were identified.
In summary, the solution enabled the early identification of a problem with the process models used for external library characterization. This granted the team ample time to address the issue proactively, avoiding downstream complications and negative impacts to project timeline and resource requirements.
The proposed methodology enables users to plug the missing data for a dataset that is starved of it, thereby providing more clarity when comparing with the golden dataset. It leverages AI to allow end-users to overcome the challenge of limited information on how the IP was created. Using this, potential issues with external .lib files where incorrect process models were utilized during the characterization process were identified. By detecting this discrepancy much earlier in the overall development workflow, corrective actions were taken based on the deviations that were identified.
In summary, the solution enabled the early identification of a problem with the process models used for external library characterization. This granted the team ample time to address the issue proactively, avoiding downstream complications and negative impacts to project timeline and resource requirements.
Event Type
Engineering Poster
Networking
TimeMonday, June 235:00pm - 6:00pm PDT
LocationEngineering Posters, Level 2 Exhibit Hall