{"id":3645,"date":"2024-06-25T20:57:44","date_gmt":"2024-06-25T20:57:44","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2024\/06\/25\/why-3d-visualization-holds-key-to-future-chip-designs\/"},"modified":"2024-06-25T20:57:44","modified_gmt":"2024-06-25T20:57:44","slug":"why-3d-visualization-holds-key-to-future-chip-designs","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2024\/06\/25\/why-3d-visualization-holds-key-to-future-chip-designs\/","title":{"rendered":"Why 3D Visualization Holds Key to Future Chip Designs"},"content":{"rendered":"
\n\t\t<\/span> <\/span> <\/span><\/span><\/p>\n

Multi-die chips, known as three-dimensional integrated circuits, or 3D-ICs, represent a revolutionary step in semiconductor design. The chips are vertically stacked to create a compact structure that boosts performance without increasing power consumption.<\/p>\n

However, as chips become denser, they present more complex challenges in managing electromagnetic and thermal stresses. To understand and address this, advanced 3D multiphysics visualizations become essential to design and diagnostic processes.<\/p>\n

At this week\u2019s Design Automation Conference<\/a>, a global event showcasing the latest developments in chips and systems, Ansys \u2014 a company that develops engineering simulation and 3D design software \u2014 will share how it\u2019s using NVIDIA technology to overcome these challenges to build the next generation of semiconductor systems.<\/p>\n

To enable 3D visualizations of simulation results for their users, Ansys uses NVIDIA Omniverse<\/a>, a platform of application programming interfaces, software development kits, and services that enables developers to easily integrate Universal Scene Description (OpenUSD)<\/a> and NVIDIA RTX rendering technologies into existing software tools and simulation workflows.<\/p>\n

The platform powers visualizations of 3D-IC results from Ansys solvers so engineers can evaluate phenomena like electromagnetic fields and temperature variations to optimize chips for faster processing, increased functionality and improved reliability.<\/p>\n

With Ansys Icepak on the NVIDIA Omniverse platform, engineers can simulate temperatures across a chip according to different power profiles and floor plans. Finding chip hot-spots can lead to better design of the chips themselves, as well as auxiliary cooling devices. However, these 3D-IC simulations are computationally intensive, limiting the number of simulations and design points users can explore.<\/p>\n

Using NVIDIA Modulus, combined with<\/a> novel techniques for handling arbitrary power patterns in the Ansys RedHawk-SC electrothermal data pipeline and model training framework, the Ansys R&D team is exploring the acceleration of simulation workflows with AI-based surrogate models. Modulus is an open-source AI framework for building, training and fine-tuning physics-ML models at scale with a simple Python interface.<\/p>\n

With the NVIDIA Modulus Fourier neural operator <\/a>(FNO) architecture, which can parameterize solutions for a distribution of partial differential equations, Ansys researchers created an AI surrogate model that efficiently predicts temperature profiles for any given power profile and a given floor plan defined by system parameters like heat transfer coefficient, thickness and material properties. This model offers near real-time results at significantly reduced computational costs, allowing Ansys users to explore a wider design space for new chips.<\/p>\n

\"\"<\/a>
Ansys uses a 3D FNO model to infer temperatures on a chip surface for unseen power profiles, a given die height and heat-transfer coefficient boundary condition.<\/figcaption><\/figure>\n

Following a successful proof of concept, the Ansys team will explore integration of such AI surrogate models for its next-generation RedHawk-SC platform using NVIDIA Modulus.<\/p>\n

As more surrogate models are developed, the team will also look to enhance model generality and accuracy through in-situ fine-tuning. This will enable RedHawk-SC users to benefit from faster simulation workflows, access to a broader design space and the ability to refine models with their own data to foster innovation and safety in product development.<\/p>\n

To see the joint demonstration of 3D-IC multiphysics visualization using NVIDIA Omniverse APIs, <\/i>visit Ansys at the Design Automation Conference<\/i><\/a>, running June 23-27, in San Francisco at booth 1308 or watch the <\/i>presentation<\/i><\/a> at the Exhibitor Forum.<\/i><\/p>\n

\t\t<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"

https:\/\/blogs.nvidia.com\/blog\/ansys-omniverse-modulus-accelerate-simulation\/<\/p>\n","protected":false},"author":0,"featured_media":3646,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/3645"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=3645"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/3645\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/3646"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=3645"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=3645"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=3645"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}