Feature identification for parameter extraction and defect detection using machine learning

2024-04-10

Advanced Lithography

AI Summary

This work addresses the time-consuming nature of parameter extraction and inspection algorithm development caused by complex 3D structures and EUV stochastic defects in advanced semiconductor nodes. We propose a generalizable deep learning framework based on high-speed scanning probe microscopy images. By leveraging machine learning to replace conventional customized image processing algorithms, this framework enables automated feature recognition, critical dimension parameter extraction, and nanoscale defect detection across diverse layers, structures, and devices. The core innovation lies in establishing a unified intelligent analysis paradigm that significantly shortens algorithm development cycles while enhancing inspection accuracy. Consequently, the proposed approach effectively satisfies the stringent requirements of high-throughput in-line process control in semiconductor wafer fabs.

Abstract

Process control of advanced semiconductor nodes is not only pushing the limits of metrology equipment requirements in terms of resolution and throughput but also in terms of the richness of data to be extracted to enable engineers to finetune the process steps for increased yield. The move towards 3D structures requires extraction of critical dimension parameters from structures which can vary largely from layer to layer. For in-line process control, the necessary automation forces the development of layer and equipment-specific dedicated image processing algorithms. Similarly, with the increase in stochastic defects in the EUV era, detection of defects at the nm scale requires the identification of features captured in low resolution to meet the throughput requirements of HVM fabs, which can again lead to custom algorithm development. With the emergence of ML-based image processing methods, this process of algorithm development for both cases can be accelerated. In this work, we provide the general framework under which the images obtained from high-speed scanning probe microscopy-based systems can be used to train a network for either feature detection for parameter extraction or defect identification.

Problem

• semiconductor metrology
• parameter extraction
• defect detection
• feature identification
• process control

Innovation

• Machine Learning
• Feature Identification
• Defect Detection
• Scanning Probe Microscopy
• Semiconductor Metrology