The three industrial manufacturers of laser diodes that are involved in this project as well as the Ferdinand-Braun-Institut, Leibniz-Institut für Höchstfrequenztechnik (FBH) face the typical challenges of the semiconductor industry in their production processes.
Rigid quality control is mandatory due to the downstream costs of defect components. While defects and defect types can be clearly identified via visual inspection, there is high personnel and financial expenditure for those inspections. In large parts, an inspection is carried out by a trained employee. It is a task that requires the highest concentration and care, but at the same time, it is monotonous and tiring.
The goal of the LaserSKI project is to solve these challenges by applying machine learning to detect and classify the defects in different stages of the production process automatically. This will
increase the efficiency and reliability of the defect inspection
lower costs
relieve employees from monotonous quality control tasks
enable companies to detect patterns in defect occurrence in real-time.