Metal fill extraction: Breaking the speed-accuracy tradeoff

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 a crucial part of the manufacturing process, ensuring uniform layer density, improving planarization, and managing thermal and stress effects. However, the added metal fill structures also introduce parasitic capacitances that can significantly impact the behavior of the circuit.

What is metal fill and why is it important?

Metal fill refers to the addition of non-functional conductive structures in the layout to ensure uniform density across the chip. This is important for the planarization process during chemical mechanical polishing (CMP), as well as for thermal management and stress management during manufacturing. Without metal fill, there would be large open areas on the chip with little to no metal, leading to uneven deposition and thickness variations that are difficult to planarize. Figure 1 presents an example of a design with many metal fill shapes populating the empty areas in between signal nets.

By adding these metal fill shapes, the density of each metal layer is evened out, enabling more effective CMP and better control over the final wafer topography. This improves the manufacturability and reliability of the chip. Metal fill also helps with thermal management by providing more conductive paths for heat dissipation, and it can help mitigate mechanical stress effects during processing.

However, the added metal fill structures also introduce parasitic capacitances that can significantly impact the behavior of the circuit. These parasitic effects can influence the chip’s timing, power integrity and overall performance. As a result, accurately modeling the impact of metal fill has become crucial for modern IC designs.

Limitations of traditional approaches

Traditionally, there has been a significant computational challenge in balancing the accuracy of parasitic extraction with the runtime required. Ignoring metal fill entirely oversimplifies the model, while extracting all parasitic components from the fill shapes is computationally expensive. This has led to two main approaches:

• Ignoring metal fill until signoff
  The simpler method is to ignore metal fill until the final signoff stage. The design is placed and routed, RC values are extracted, and static timing analysis (STA) is performed without considering metal fill. When the design is believed to be ready, metal fill is inserted, and RC extraction is rerun to account for the capacitive coupling. However, this late-stage addition of metal fill often results in timing violations that require numerous design modifications to resolve, delaying the project schedule.
• Inserting metal fill during layout
  The second approach is to perform metal fill insertion after each place and route step, enabling extraction and STA to account for the effects on every iteration. However, this adds significant time to the layout loop, as inserting metal fill on every layer can add hours to each iteration, slowing down the development process. The metal fill would still need to be verified during signoff, but there should be fewer violations that require design modifications.

These two approaches hint at an underlying tradeoff: modeling accuracy vs. computational efficiency. The most detailed modeling includes all metal fill as part of the parasitic extraction procedure with high accuracy, but at the cost of very high computational resources and processing time. Approximations will reduce the computational load, but at a cost to accuracy, leading to potential design errors not caught during simulation phases.

There are several ways to control the tradeoffs between accuracy and computational cost. For example, nets can be modeled as grounded or left floating, a decision with significant impacts on the accuracy of parasitic values calculated. Consider the options:

• Floating, no reduction: Keep all fill shapes as floating in simulations and extract all of them for a more precise modeling of the actual physical design.
• Reduction: Reduces the parasitic network of the fill shapes by analyzing the whole design to understand the impact of each fill shape. This generates a reduced netlist and maintains parasitic network equivalence to enable faster simulations.
• Grounding: Assume floating nets are grounded to simplify the extraction process and reduce computational overhead.
• Ignoring: Ignore all metal fill shapes and extract only signal nets. This is usually only used for debugging purposes.

The need for a new solution

These traditional flows are no longer sufficient for today’s large and complex chips. Designers need a solution that does not delay the schedule but still delivers high correlation to real metal fill in terms of extraction and timing accuracy

The solution is a smart and adaptive metal fill extraction technique that selectively focuses on the most impactful parasitic capacitances. This approach is an enhancement to the “reduction” method and can provide over 4x runtime improvements compared to conventional methods, while maintaining minimal impact on accuracy (figure 2).

An adaptive metal fill extraction technique dynamically adjusts the level of detail based on the design context, such as the density of signal nets versus fill shapes. This  context-aware solution improves designer efficiency as semiconductor technology continues to scale and design complexity increases.

Conclusion: Enhancing productivity and reliability

Smarter metal fill extraction ensures that there are no unpleasant surprises late in the schedule, as the design team can be confident in the fast, accurate solution provided by the adaptive metal fill extraction technique. As semiconductor technology continues to advance, this type of smart, context-aware approach will become increasingly important for managing the complexity of metal fill and its impact on design implementation. This helps ensure the final IC product meets stringent performance and reliability requirements, even as semiconductor technology continues to scale.

About the author, Shehab Ashraf

Shehab is a product engineer for Calibre Design Solutions at Siemens Digital Industries Software, specializing in parasitic extraction. He received his BE in Electrical and Electronics Engineering from The German University in Cairo.

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