AI/ML assisted fault detection in foundry processed devices
Description
Highly accurate fault detection in foundry produced microelectronics is crucial to ensuring quality of devices that leave the foundry. However, current defect detection flows are human-centric, which produces a bottleneck. The objective of this project is to leverage recent advances in AI/ML to develop automated techniques that can 1) identify manufacturing defects in microelectronics using imagery collected at the foundry, and 2) determine whether the identified defect will impact the performance of the manufactured component.
Students
Advisors
What students will learn
Students will learn about manufacturing defect detection algorithms, machine learning techniques, and microelectronics fabrication.