Deep learning for fast segmentation and critical dimension metrology & characterization enabling AR/VR design and fabrication

TL;DR

This paper presents a deep learning approach, fine-tuning a pre-trained segmentation model with electron microscopy images, to enable fast, accurate segmentation and critical dimension extraction for AR/VR component fabrication.

Contribution

It introduces a novel fine-tuning method for the Segment Anything Model using electron microscopy data, improving segmentation accuracy and enabling precise CD extraction for industrial applications.

Findings

Effective zero-shot segmentation of microscopy images.

Accurate extraction of critical dimensions from segmented regions.

Enhanced industrial process optimization through deep learning.

Abstract

Quantitative analysis of microscopy images is essential in the design and fabrication of components used in augmented reality/virtual reality (AR/VR) modules. However, segmenting regions of interest (ROIs) from these complex images and extracting critical dimensions (CDs) requires novel techniques, such as deep learning models which are key for actionable decisions on process, material and device optimization. In this study, we report on the fine-tuning of a pre-trained Segment Anything Model (SAM) using a diverse dataset of electron microscopy images. We employed methods such as low-rank adaptation (LoRA) to reduce training time and enhance the accuracy of ROI extraction. The model's ability to generalize to unseen images facilitates zero-shot learning and supports a CD extraction model that precisely extracts CDs from the segmented ROIs. We demonstrate the accurate extraction of binary images from cross-sectional images of surface relief gratings (SRGs) and Fresnel lenses in both single and multiclass modes. Furthermore, these binary images are used to identify transition points, aiding in the extraction of relevant CDs. The combined use of the fine-tuned segmentation model and the CD extraction model offers substantial advantages to various industrial applications by enhancing analytical capabilities, time to data and insights, and optimizing manufacturing processes.