AI4X 2025 Papers

As semiconductor devices continue to shrink to the nanoscale, ensuring their structural accuracy becomes increasingly critical. Optical imaging techniques play a key role in defect detection in semiconductor manufacturing. However, these optical techniques are fundamentally limited by the diffraction limit, which restricts the resolution of features smaller than half the illumination wavelength. While deep learning has shown promise in pushing beyond this limit, conventional models often rely on pixel-wise loss functions that fail to capture the global structure of semiconductor patterns. This can result in broken lines, missing features, and inaccurate defect detection. In this work, we introduce a topology-preserving deep learning framework tailored for high-resolution optical imaging in semiconductor characterization. By embedding topological constraints through persistent homology in a differentiable loss function, our model enforces structural consistency across the entire image. Experimental results demonstrate an improvement of nearly 30% in the quantifier metric against ground truth compared to traditional methods on the nano-particle dataset, accurately resolving features as small as 0.16λ (100 nm) under 640 nm illumination. The model further reduces false disconnectivities by 5% in nano line localization. This topologically-aware approach provides a robust and non-destructive solution for subwavelength imaging in semiconductor metrology, enabling more reliable inspection in precision manufacturing.

The rapid expansion of scientific literature necessitates developing efficient data extraction and analysis methods. This study presents an innovative approach to automating the extraction of cryoprotectant information from scientific publications using a generative pretrained transformer (GPT) model integrated with a Telegram bot interface. Our system processes and analyzes scientific articles to identify and extract relevant data on cryoprotectants and bacteria, significantly reducing the time required for researchers to gather essential information. Our method optimizes the workflow for researchers in cryopreservation and related fields by utilizing modern artificial intelligence technologies, specifically large language models. The Telegram bot, designed to be user-friendly, provides a comfortable and easy platform for quick data access, enhancing scientific research efficiency. The study's methodology involves data preparation, algorithm development, and system validation using a substantial data set of scientific articles. Results demonstrate the model's capability to accurately recognize and extract critical information, although some limitations in term specificity were noted. Our findings suggest that further refinement and training of the model can enhance its accuracy and reliability for specialized scientific applications.