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Preventing Thermal Management Issues

Excessive heat can cause mechanical stress and warping, particularly if temperatures continually exceed optimal ranges. Although heat sinks and other cooling structures dissipate heat, these components also add to device area and cost. A well-planned multi-die system that follows an iterative design process and includes integrated thermal management solutions is a far more efficient way of avoiding potential thermal issues.

In addition, design teams can simulate and analyze thermal behavior in diverse deployment scenarios while implementing architectural revisions to optimize chip layout and improve heat dissipation. Since each die has its own software stack, holistically modeling hardware and software is essential to implementing a design that¡¯s fundamentally robust and thermally sound. Moreover, chip companies must monitor thermal behavior throughout the design process, including during RTL, synthesis, and place and route.

Optimizing heat dissipation and minimizing thermal challenges will ultimately evolve into more of an automated process. Harnessing new EDA tools to build better, faster, and more efficient multi-die systems will become a crucial prerequisite for designers as  fades into the distant past and silicon complexity continues to increase almost exponentially.

Accelerating Multi-Die Systems Adoption

AI-driven EDA and industry collaboration were two key themes highlighted during a recent SNUG Silicon Valley 2023 panel titled ¡°Multi-Die Systems: The Biggest Disruption in Computing for Years.¡± As panel participants emphasized, the coherent convergence of innovations across the semiconductor industry by EDA, IP, foundry, and OSAT leaders is critical to overcoming thermal design challenges and accelerating the adoption of multi-die systems.

According to John Lee, Ansys GM and VP, packing more and more transistors into multi-die systems creates a steep multi-physics learning curve for designers running thermal simulations. Nevertheless, Lee said the semiconductor world would soon see trillion-transistor chip architectures, with AI and machine learning (ML) helping design teams simulate thermal behavior for over 10 trillion geometries meshed into more than 100 trillion elements. As Lee explained, these advanced designs will ¡°require a village¡± to architect and test successfully, which is why industry collaboration and additional specifications for multi-die systems are crucial.

Fran?ois Piedn?el, distinguished mSoC chief architect at Mercedes-Benz Research, stated that thermal challenges offer an important opportunity for new multi-die standards and specifications, as well as further cross-industry collaboration. As Piedn?el explained, these items are essential for advanced driver-assistance systems (ADAS), which typically include silicon components manufactured by different vendors. A collaborative approach, said Piedn?el, will make it significantly easier for automotive design teams to leverage AI-driven EDA and holistically analyze the thermal behavior of multi-die systems and systems on chips (SoCs) throughout an entire vehicle.