The internship aims to improve semiconductor defect dispositioning by applying Transformer-based Deep Learning Classification Methods.
Requirements
- Familiarity with deep learning program environments such as TensorFlow, PyTorch, ONNX, and OpenVINO)
- Proficiency in Python, and visualization packages
- Familiarity with prior experience in evaluating deep learning methods for industrially relevant problems
Responsibilities
- Select and evaluate Transformer Networks from the literature
- Evaluate traditional approaches employed by the team
- Explore Conditional Classification Models for defect classification work
- Examine explainability tools beyond traditional GradCam methods
Other
- Currently enrolled in an accredited college or university in industrial engineering, electrical engineering, or computer science/engineering, or similar degree program
- Must have successfully completed up to and including year 2 of a PhD program
- Must be familiar with deep learning program environments such as TensorFlow, PyTorch, ONNX, and OpenVINO
