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Cadence Debuts Celsius Studio for In-Design Thermal Optimization

Cadence Debuts Celsius Studio for In-Design Thermal Optimization
by Bernard Murphy on 02-21-2024 at 6:00 am

Continuing the multiphysics theme, I talked recently with Melika Roshandell (Product Management Director at Cadence) on the continuing convergence between MCAD and ECAD. You should know first that Melika has a PhD in mechanical engineering and an extensive background in thermal engineering at Broadcom and Qualcomm, all very relevant to this topic. One immediate revelation from this discussion for me was that thermal analysis and optimization for chips and systems is commonly handled by mechanical engineers working cooperatively with the electrical design teams. Makes sense, but that difference in expertise and disciplines can and often does cause significant speed bumps between these elements of design, leading to inefficiencies in execution and optimization. Celsius Studio aims to flatten these speed bumps.

Cadence Debuts Celsius Studio for In-Design Thermal Optimization

A quick recap on the multiphysics need

There’s an old joke in physics. A dairy farmer asks the local university for help to understand why milk production has dropped at his farm. After hosting a tour of his farm, detailed discussions, and much study back at the university he receives a letter from the theoretical physics department. They tell him that they have found a solution, but it only works for spherical cows in a vacuum. Point being that physicists must greatly simplify a problem, prioritizing just one component to find an analytic solution.

Computer-based numerical analysis doesn’t suffer from that limitation, forgoing exact answers for approximate answers though to whatever precision is needed. It is also not limited to consider only physical effect at a time. Which is just as well because in chip and system design, multiple physical factors are significant at all levels of design and can’t be neatly separated.

Electrical activity unavoidably generates heat (second law of thermodynamics): in a transistor, a block of logic, a chip/chiplet, a package, on a board and in a rack. Heat is generated locally in areas of active usage which can lead to incorrect behavior or physical damage if not effectively dissipated. One way to reduce heating is to lower clock speeds until sufficiently cooled but that reduction also compromises performance. For optimal operation, heat generated by electrical activity (dynamic and leakage) must be dissipated passively (thermal diffusion, radiation, and convection) and/or actively (forced air or liquid cooling). Multiple types of physics must be analyzed together.

Another important consideration is the tendency of structures to warp under heating. Chips/chiplets are fabricated with multiple layers of materials, each with different thermal expansion properties. Chiplets sit on top of interposers and other layers, inside a package sitting on top of a multilayer PCB, and so on – more different materials with different expansion coefficients. When two (or more) connected layers expand under heating, one will expand more than the other. If this differential expansion is big enough the structure will warp. That adds stress to electrical connections between layers which can fracture and disconnect. Problems of this nature do not self-heal after cooling; the only way to fix your phone if connections break is to get a new phone. More multiphysics analysis is needed.

One more wrinkle makes the thermal management problem even more complex. All this analysis must work across a very wide scale range, from tens of microns in the IC design, to tens of centimeters on a board, up to meter ranges in a rack. Heat can be generated at all levels and cooling must be effective at all levels. Multiphysics analysis must also perform at multi-scale.

Celsius Studio targets comprehensive in-design analysis

Celsius Studio integrates together thermal analysis and implementation insights from Innovus for digital circuits, Virtuoso for custom/analog circuits, Integrity for 3D-ICs, AWR for microwave ICs, and Allegro for board design. These insights guide power overall thermal and stress analysis together with heat reduction strategies, placement optimization, and thermal via and temperature sensor placement.

Thermal and stress modeling are accomplished through finite element analysis (FEA), with meshes designed to support necessary accuracies from fine-grained to coarse-grained structures across that wide scale range. Heat dissipation through convection and/or through active cooling (fans, etc.) are modeled in the Cadence Celsius EC Solver.

Obviously, this analysis requires MCAD models which can be created in the tool or can be imported from multiple popular MCAD formats. Sounds easy but historically, according to Melika, difficulties in seamlessly coupling MCAD and ECAD have contributed significantly to those speed bumps. In Celsius Studio, Cadence in-house MCAD and ECAD experts have reduced the import effort from days to negligible impact on the analysis flow. Therefore providing a streamlined path to thermal, stress, and cooling analysis on boards and in-rack.

That streamlined path makes in-design analysis (IDA) a much more realistic proposition. Previous over-the-wall exchanges between engineering and thermal engineering obviously limited opportunities for co-design/optimization, tending to best guess estimates to guide thermal teams followed by a scramble at the end to align against final analytics from the electronics teams. Now with faster turn-times to import mechanical model updates, co-optimization through design becomes feasible, reducing the risk of late scrambles and schedule/BOM changes.

Comprehensive coverage without sweeping parameters

Faster turn-times also allow for AI-enabled analysis. I’m going to go out on a limb here with a little of my own speculation. To analyze/optimize a complex design with many parameters you can sweep those parameters over all possible settings and combinations. However the complexity of sweeping expands exponentially as more parameters are added. There is a concept in Design of Experiments called Covering Arrays which we have written about in an Innovation blog, to massively reduce the number of combinations you must consider while only modestly reducing coverage. There is only one problem – figuring out the right options to pick requires a lot of human ingenuity. Machine learning could be another way to get there, across many more parameters.

I don’t know if this is the method behind Optimality or other tools of this nature, but I do think some related technique may play a role. Especially since this method can be applied to any problem, mechanical or electronic, to select a small and manageable subset from an otherwise impractical sweep range, to achieve near-optimal coverage in analysis 😀

You can read more about Celsius Studio HERE.

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