Relating human and AI-based detection limits in scanning electron microscopy dimensional metrology.

Abstract

Background: Nanoscale measurements of critical dimensions in semiconductor manufacturing rely on scanning electron microscopy (SEM) and SEM image analyses. The acquisition of SEM images requires a low primary electron beam current and a low dose of the SEM imaging microscope to avoid integrated circuit (IC) sample charging and inflicted damage to sensitive IC structures. These requirements inevitably result in noisy, low-contrast images, which can make traditional SEM image analyses no longer viable.

Aim: With the advancement in computational hardware and artificial intelligence (AI) models, IC structure detection via AI-based SEM image segmentation can extend the viability of these measurements from noisy, low-contrast images. However, the use of AI models raises questions about the detection limits of extracted measurements and the confidence in such measurements.

Approach: Our approach is to relate SEM image quality characteristics with AI-based object segmentation accuracy to establish detection limits of AI-based models and their relationships to human detection limits. Using SEM image simulation software, we create six image sets of quasi-circular objects on a substrate with varying noise and contrast characteristics. These sets of SEM images are characterized by 25 image quality metrics and then used to train and evaluate three AI models. The 25 SEM image quality characteristics and three AI model accuracy metrics per SEM image define the mapping between the quality of input SEM images and the performance of the trained AI models.

Results: We used the mapping to establish the detection limits of trained AI models with respect to a required confidence and then relate the human detection limits to the trained AI model detection limits. The human detection limit was established by Rose as the minimal signal-to-noise ratio (SNR = 5) to reliably delineate the shape and size of objects in an image. We matched the signal-to-noise ratio (SNR) defined by Rose to image quality characteristics and demonstrated the upper and lower SNR bounds for three AI models with respect to the human detection limit and for a specified confidence.

Conclusion: We establish a method to determine the detection limits of AI-based SEM dimensional metrology. The study is relevant to semiconductor vendors and consumers of AI models because critical dimension measurements are derived from noisy and low-contrast SEM images using AI models with varying performance characteristics. Given a measured SEM image with estimated noise and contrast characteristics, each AI model will be characterized by unique detection limits that can be trusted in semiconductor production. Our method enables improving the trust in critical dimensions while using advanced AI models.