A study by the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences， titled "Data-driven precise extraction and division of spatial frequency band errors," published in the journalOptics and Laser Technology, reports a deep learning framework for optical analysis and achieves automated categorization of surface imperfections for advanced manufacturing.

In the realm of precision optical manufacturing, achieving an absolutely flawless surface remains a paramount objective. However, microscopic surface errors inevitably occur during the fabrication processes. These optical surface errors are typically analyzed across various spatial frequencies, as different frequency bands affect optical systems in distinct ways. Broad, low-frequency errors tend to distort the overall shape of the transmitting light beam, while fine, high-frequency ripples scatter the light and severely reduce the final image contrast. Traditionally, engineers rely on manual filtering techniques to separate and evaluate these different frequency bands. This conventional approach proves highly inefficient and introduces significant human subjectivity. Moreover, the entire optical industry lacks a unified, universal standard for dividing these spatial frequency bands, which creates a critical bottleneck when testing and standardizing high-end optical components.

To overcome these enduring challenges, the research team introduces a novel data-driven methodology powered by advanced artificial intelligence. Moving away from traditional manual intervention, the researchers develop a sophisticated convolutional neural network enhanced by specialized attention mechanisms. This intelligent system automatically learns the complex topographic patterns of optical surface errors by processing extensive datasets. Instead of applying rigid, pre-defined mathematical filters, the network dynamically processes the topographic data and focuses specifically on the most critical spatial features. By employing a dual-stream architecture, the computational model simultaneously evaluates both macroscopic structural deviations and microscopic surface roughness, ensuring a comprehensive analysis of the entire optical element.

During the analytical process, the data-driven framework automatically determines the optimal boundaries for various frequency bands based on the intrinsic geometric characteristics of the provided surface data. This capability effectively eliminates the guesswork and inconsistency inherent in manual filtering procedures. The model mathematically separates the overlapping error signals, mapping them into clearly defined spatial categories. Consequently, the intelligent network establishes a highly consistent, objective, and reproducible standard for error extraction. This adaptive capability allows the framework to evaluate diverse types of optical components under a single unified standard, bridging the gap between theoretical error definitions and practical manufacturing diagnostics.

The experimental implementations show that the proposed data-driven framework extracts and divides spatial frequency band errors with exceptional precision and computational speed, vastly outperforming traditional manual techniques. The intelligent evaluations maintain strict objectivity, completely removing human bias from the quality control pipeline. By establishing a standardized and automated evaluation method, this research significantly streamlines the inspection and fabrication processes for complex optical surfaces. Ultimately, this methodology provides a reliable and highly efficient diagnostic tool for the precision optics industry, contributing to the continued advancement of high-performance imaging systems, space exploration telescopes, and modern laser technology.