Amit Choudhary

Failure analysis (FA) in microelectronics is becoming increasingly complex due to the miniaturization of components and the growing need for precise defect detection. Traditional FA workflows often rely on manual inspection, which are time-intensive and prone to variability. This study explores the integration of digital workflows and machine learning (ML) techniques to enhance FA efficiency.

An ML-based approach was developed for automated image analysis, ensuring consistency across multiple samples and integrating seamlessly into an external microscopy software platform for a fully digital workflow. The model demonstrated improved detection accuracy, ranging from 88% to 96%, compared to conventional image processing techniques, while also being 30x to 40x more time-efficient. This advancement enables early identification of defects, enhancing the overall FA process. Additionally, an ML pipeline was implemented to facilitate automated feature extraction and defect localization, further streamlining analysis. These ML-driven methods significantly optimize FA workflows by reducing manual effort, increasing defect detection precision, and enabling data-driven decision-making.

Amit Kumar Choudhary is currently working as a Software Engineer (ML) at Matworks GmbH and has experience in developing business-specific solutions related to automated microscopy using machine learning and computer vision techniques.