Amit Choudhary

Failure analysis (FA) in microelectronics is increasingly challenging due to miniaturization and the need for precise defect localization. Traditional methods rely on manual, time-intensive inspection. We propose a digital workflow using a domain-specific multi-task model for joint detection and segmentation of defect such as voids and cracks. This enables multi-scale learning across devices and resolutions, improving generalization while reducing annotation effort and speeding transfer learning. The developed workflows when validated against expert-annotated data using metrics such as intersection over union (IoU) and F1-score shows detection accuracies of 82–93% across two use cases with varying defect types and imaging conditions. Processing speed improved by over 30x compared to conventional pipelines due to automated feature extraction and reduced model tuning. Results indicate a reduction of over 70% in total effort, primarily due to minimizing the need for extensive pixel-level labeling and eliminating the requirement for separate models for each defect type (e.g. voids). Overall, the approach streamlines FA and enables scalable, high-throughput, data-driven defect analysis.

Dr. Ing Amit Kumar Choudhary is currently working as a Head of AI Applications at Matworks GmbH and has experience in developing business-specific solutions related to automated microscopy using AI approaches and computer vision techniques. He has background in materials microscopy, image analysis, machine learning.