Detection and metrology of micro-nano particles through the integration of image and laser modes

Abstract

Micro-nano particles are an indispensable component in various industrial and scientific applications. Accurate granular information is critical for manufacturing products involving these particles. This study presents a novel detection method that merges image and laser modes to achieve precise size and shape determination of micro-nano particles. The proposed method integrates adaptive image analysis with laser particle size fitting to ensure accurate feature detection. Experimental results using ternary precursor samples demonstrate that the fusion mode outperforms both the laser and image modes in terms of size and shape accuracy. The errors of D10, D50, and D90 of the fusion mode are 4.89%, 1.88%, and 2.94%, respectively, which are significantly lower than those obtained using the laser or image mode alone. Additionally, the fusion mode retains the particle shape information from the image mode. The fusion model offers a promising approach for dual-mode detection of micro-nano particles, demonstrating enhanced robustness compared to single-mode methods. Furthermore, the study introduces an image processing method based on a BP neural network classification model that accurately classifies and detects agglomerated and non-agglomerated particles. The classification accuracy reaches 94.98%, and the particle size information remains within the acceptable range.