As semiconductor technology scales and device complexity increases, accurately modeling the parasitic effects of metal fill has become critical for circuit performance, power integrity and reliability. Metal fill is crucial in manufacturing to ensure uniform layer density, improve planarization, thermal management and stress management. However, metal fill structures also introduce parasitic capacitance that can impact circuit behavior. Traditionally, there has been a significant computational challenge in balancing the accuracy of parasitic extraction with the runtime required; treating all metal fill as grounded oversimplifies the model, while extracting all parasitic components is computationally expensive.
This paper discusses the conventional extraction techniques for metal fill shapes in different circuit designs, stating the differences between them and the impact of using each one. An adaptive, smart technique, based on one of these conventional techniques, is also discussed in the paper, detailing what is different about, when it would be best to use and what kind of improvement to expect when using it versus the conventional techniques. This alternative extraction technique that selectively extracts only the most impactful parasitic capacitances from the metal fill, achieving over 4x runtime improvements compared to conventional methods, while maintaining minimal impact on accuracy. The adaptive technique dynamically adjusts the level of detail based on the design context, such as the density of signal nets versus fill shapes. This smart, context-aware solution improves designer efficiency as semiconductor technology continues to scale and design complexity increases.