Deep-Learning-Driven Image Reconstruction for Comprehensive Geometric Measurement of High-Aspect-Ratio Blind Holes

Accurately measuring geometric parameters of high-aspect-ratio blind holes is a critical requirement for microelectronics and micro–nanooptics applications. Conventional measurement techniques are restricted by limited measurement precision, incomplete measurable geometric indicators, bulky optical hardware, and potential irreversible damage to test specimens. To tackle these drawbacks, this paper proposes a deep-learning-driven image reconstruction method for full-parameter geometric measurement of high-aspect-ratio blind holes. In the proposed method, a Denoising Convolutional Neural Network (DnCNN) is deployed to restore low signal-to-noise ratio (SNR) microscopic images captured at blind hole bottoms. Meanwhile, Laplacian variance sharpness evaluation coupled with local quadratic polynomial fitting is adopted to boost the precision of Z-axis focal positioning and depth calculation. A dedicated machine vision measurement system is custom-developed, where annular ring illumination is integrated to strengthen light irradiation at hole bottoms and guarantee high-quality image acquisition. To address the scarcity of authentic paired clean-noisy training data, high-clarity surface micrographs are artificially degraded in reverse to synthesize low-quality counterparts, forming a dataset containing 2000 image pairs for DnCNN training and optimization. Furthermore, a dedicated feature extraction pipeline combining image preprocessing, Otsu-based adaptive threshold segmentation and least-squares ellipse fitting is designed to extract core geometric metrics, including microhole diameter, depth, taper angle, and relative ellipticity deviation. Experimental validation reveals that the absolute measurement deviations of the mean measured top diameter, bottom diameter, and depth reach 4 μm, 7 μm, and 8 μm, respectively, with a calculated depth-to-diameter-ratio error of 0.06. The results demonstrate that this method delivers a high-precision, low-cost non-destructive micrometer-scale solution for comprehensive geometric inspection of high-aspect-ratio blind holes.