Data analytics on semiconductor ion implantation processes

Abstract

This project is a spin-off from an industrial project on the application of Robotic Process Automation (RPA) for a Semiconductor Manufacturing firm in Singapore. The manufacturing process of focus in this project is ion implantation.

This project seeks to support the main project and the business sponsor by providing a case study of advance data analytics application in the firm’s digitization efforts. The problem statement defined in this project is to investigate possible factors and causes of failure in ion implantation, and develop a predictive model based on identified factors.

Descriptive analytics were conducted in depth to investigate the relationship between different factors - such as operation, operation type, gas specie, gas change and beam energy - and the process outcome. Then, predictive models were developed based on the findings and the raw data from the Ion Implanter. In building the models, four different classification models were trained and tested, and their effectiveness was measured using metrices such as f1-score and Jaccard similarity score.

To conclude the report, all findings and the best-performing model were presented. Further recommendations to enhance the model and improve overall usefulness were also provided.