Presentation
Enhancing PDK Library Validation with Machine Learning. A Novel Approach to Layout Comparison
DescriptionMaintaining the accuracy and consistency of Process Design Kits (PDKs) in the rapidly evolving semiconductor design industry is critical for ensuring high-quality integrated circuit (IC) production. Conventional techniques for PDK library comparisons, like rule-based checks and manual inspections, take a lot of time and are prone to human mistakes.
More specifically, PDK models are based on silicon data from "Golden GDS" layouts, which serve as the benchmark for model accuracy. As a device evolves, its physical layout (PCell) may need updates to accommodate model fine tuning or improve performance. Ensuring these updates remain consistent with the original Golden GDS is crucial for maintaining model accuracy.
This paper presents a novel method to improve the internal layout comparison of PDK libraries using machine learning. A regular XOR comparison between the golden GDS and the reference GDS would yield a lot of false errors and the manual review of layout variations during the lifecycle is time consuming and resource intensive to categorize changes as either expected or unexpected. Our novel approach, however, achieves considerable improvements in efficiency and reliability by streamlining the discovery of inconsistencies within PDK libraries through multiple supervised machine learning techniques.
More specifically, PDK models are based on silicon data from "Golden GDS" layouts, which serve as the benchmark for model accuracy. As a device evolves, its physical layout (PCell) may need updates to accommodate model fine tuning or improve performance. Ensuring these updates remain consistent with the original Golden GDS is crucial for maintaining model accuracy.
This paper presents a novel method to improve the internal layout comparison of PDK libraries using machine learning. A regular XOR comparison between the golden GDS and the reference GDS would yield a lot of false errors and the manual review of layout variations during the lifecycle is time consuming and resource intensive to categorize changes as either expected or unexpected. Our novel approach, however, achieves considerable improvements in efficiency and reliability by streamlining the discovery of inconsistencies within PDK libraries through multiple supervised machine learning techniques.
Event Type
Engineering Presentation
TimeTuesday, June 2411:00am - 11:15am PDT
Location2012, Level 2
AI
Back-End Design