Inverse prediction of capacitor multiphysics dynamic parameters using deep generative model

Inverse prediction of capacitor multiphysics dynamic parameters using deep generative model

2023 IEEE 73rd Electronic Components and Technology Conference (ECTC)

10.1109/ECTC51909.2023.00313

http://dx.doi.org/10.1109/ectc51909.2023.00313

Kart-Leong Lim, Rahul Dutta, Mihai Rotaru

03 August 2023

Lim, K.-L., Dutta, R., & Rotaru, M. (2023, May). Inverse prediction of capacitor multiphysics dynamic parameters using deep generative model. 2023 IEEE 73rd Electronic Components and Technology Conference (ECTC). https://doi.org/10.1109/ectc51909.2023.00313

Finite element simulations are run by package design engineers to model design structures. The process is irreversible meaning every minute structural adjustment requires a fresh input parameter run. In this paper, the problem of modeling changing (small) design structures through varying input parameters is known as inverse prediction. We demonstrate inverse prediction on the electrostatics field of an air-filled capacitor dataset where the structural change is affected by a dynamic parameter to the boundary condition. Using recent AI such as deep generative model, we outperformed best baseline on inverse prediction both visually and in terms of quantitative measure.

Publisher Copyright

There was no specific funding for the research done

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https://oar.a-star.edu.sg/communities-collections/articles/20261

2377-5726

Institute of Microelectronics