Electrothermal Simulation of 3D ICs Siemens and Nvidia Automate Thermal Analyses in Chip Design

Source: Press release Celine Hummel | Translated by AI 2 min Reading Time

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With stacked chips, heat dissipation is becoming an increasing challenge. Siemens and Nvidia have therefore developed an AI-powered EDA workflow that automates electrical and thermal analyses. According to Siemens, this allows for the evaluation of up to 50 times more design variants.

Thermal simulation of a 3D IC visualizes the temperature distribution within the chip structure. Siemens and Nvidia aim to automate and accelerate such electrothermal analyses using AI-powered EDA workflows.(Image:  Siemens EDA)
Thermal simulation of a 3D IC visualizes the temperature distribution within the chip structure. Siemens and Nvidia aim to automate and accelerate such electrothermal analyses using AI-powered EDA workflows.
(Image: Siemens EDA)

The more chiplets and semiconductor components are stacked on top of each other within a package, the more difficult it becomes to dissipate the resulting heat. Local temperature spikes can impair the performance and reliability of the components. Therefore, developers must take electrical and thermal interactions into account as early as the design phase of complex 3D-ICs.

To this end, Siemens Digital Industries Software, in collaboration with Nvidia, unveiled an AI-powered workflow on October 21, 2026, that automates these analyses across multiple EDA tools. Instead of manually preparing individual simulation steps, transferring data between programs, and repeatedly adjusting designs, AI agents take over the coordination of the tools.

Seven EDA Tools in a Single Workflow

The new approach integrates nine work steps across seven EDA tools from Siemens. These include, among others, mPower for power analysis, Calibre 3DThermal for thermal analysis, and Simcenter Flotherm for simulating thermal behavior at the system level. Other tools take into account, for example, temperature-dependent parasitic effects and the electrical behavior of the circuits.

The AI agents coordinate the analyses and assist in the search for suitable design variants. In this process, the engineer can modify, among other things, the component layout, material structures, power budgets, and thermal constraints. Evaluation continues to be performed using physics-based simulation tools. NVIDIA’s CUDA-X libraries accelerate parts of the calculations.

Up to 95 Percent Less Engineering Effort

According to Siemens, benchmark studies showed that engineering effort was reduced by up to 95 percent. Setting up the relevant tools took less than five minutes instead of several days. At the same time, up to 50 times more design variants could be analyzed than with conventional manual processes.

This is intended to help developers identify thermal bottlenecks earlier and compare different packaging and design options more systematically. This could simplify design optimization, particularly for 3D-ICs, where power consumption, heat generation, and package design are closely interrelated. The improvements mentioned are based on benchmark studies conducted by the manufacturer. The extent to which these time savings can be applied to specific development projects depends, among other factors, on the complexity of the design and the analysis tools used. 

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