Feature identification for parameter extraction and defect detection using machine learning

arXiv:2610.05812v1 Announce Type: new 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.

Related

• Image resolution enhancement for advanced semiconductor nodes
• Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors
• Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination
• Dynamics-Encoded Deep Learning for Robust System Identification and Parameter Estimation

Source: arXiv cs.LG | 2026-10-06