Scanning Electron Microscopy-based Automatic Defect Inspection for Semiconductor Manufacturing: A Systematic Review

TL;DR

This systematic review analyzes SEM-based automatic defect inspection algorithms in semiconductor manufacturing, highlighting the shift from reference-based to deep learning methods and discussing key challenges and future directions.

Contribution

It provides a comprehensive categorization and analysis of 103 recent papers, revealing trends, challenges, and promising future research directions in SEM image analysis for defect detection.

Findings

Deep learning algorithms surpassed reference-based methods after 2020.

Inspection algorithm components include setup, pre-processing, feature extraction, and prediction.

Challenges involve balancing inspection speed, manual setup reduction, and processing throughput.

Abstract

In this review, automatic defect inspection algorithms that analyze Scanning Electron Microscopy (SEM) images for Semiconductor Manufacturing (SM) are identified, categorized, and discussed. This is a topic of critical importance for the SM industry as the continuous shrinking of device patterns has led to increasing defectivity and a greater prevalence of higher-resolution imaging tools such as SEM. Among others, these aspects threaten to increase costs due to increased inspection time-to-solution and decreased yield. Relevant research papers were systematically identified in four popular publication databases in January 2024. A total of 103 papers were selected after screening for novel contributions relating to automatic SEM image analysis algorithms for semiconductor defect inspection. These papers were then categorized based on the inspection tasks they addressed, their evaluation metrics, and the type of algorithms used. A notable finding from this categorization is that reference-based defect detection algorithms were the most popular algorithm type until 2020 when Deep Learning (DL)-based inspection algorithms became more popular, especially for defect classification. Furthermore, four broader research questions were discussed to come to the following conclusions: (i) the key components of inspection algorithms are set up, pre-processing, feature extraction, and final prediction; (ii) the maturity of the manufacturing process affects the data availability and required sensitivity of inspection algorithms; (iii) key challenges for these algorithms relate to the desiderata of minimizing time-to-solution which pushes for high imaging throughput, reducing manual input during algorithm setup, and higher processing throughput; and (iv) three promising directions for future work are suggested based on gaps in the reviewed literature that address key remaining limitations.