CIFRE - Développement de méthodes IA adaptées aux structures pour la modélisation microélectronique - H​/F

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• TCAD simulation allows modeling microelectronic components to predict and optimize their performance. This simulation domain produces 2D or 3D grid structures in space.
• Integrating artificial intelligence (AI) techniques applied to grid structures from TCAD simulations opens new perspectives to accelerate analysis, prediction, and optimization. Implementing inverse design methods on grid structures used in TCAD could quickly identify input parameters related to fabrication and design (layout) to achieve desired electrical output performances.
• Previous work has focused on using 1D simulation data and optimization methods. This CIFRE thesis topic concentrates on 2D/3D grid structures, which would optimize material shapes and morphologies as well as dopant profiles in components.
• The objective of this thesis is to develop methods for using artificial intelligence with 2D/3D TCAD grid data, such as graph neural networks (GNN) adapted to this type of data, or generative AI methods.

• Engineer or Master 2 student interested in research in the industrial world.
• Knowledge of artificial intelligence algorithms and neural networks.
• Knowledge of Python programming.

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