AI for Modeling and Topographic Analysis of Electronic Chips

This PhD position at Université Grenoble Alpes, hosted within the LETI laboratory, focuses on applying artificial intelligence to improve wafer surface inspection and microfabrication processes in the semiconductor industry. The candidate will design and optimize machine learning algorithms, such as convolutional neural networks and generative models, to predict topographical defects with high precision using optical interferometry data. The work aims to enhance computational efficiency and generalization while reducing manufacturing costs. The successful applicant will work closely with research staff at CEA and collaborate within a prominent microelectronics research environment.

• Develop and train artificial intelligence algorithms to characterize and model semiconductor wafer topography
• Design predictive models using convolutional neural networks, generative models, or hybrid approaches for defect detection
• Optimize machine learning models to achieve fast inference and robust generalization in industrial settings
• Analyze optical interferometry data to detect topographical surface defects such as erosion and dishing
• Collaborate with researchers to integrate AI solutions into microfabrication and production workflows