High-aspect-ratio structures have become increasingly important in MEMS devices. In situ, real-time critical dimension and depth measurement for high-aspect-ratio structures is critical for optimizing the deep etching process. Through-focus scanning optical microscopy (TSOM) is a high-throughput and inexpensive optical measurement method for critical dimension and depth measurement. Thus far, TSOM has only been used to measure targets with dimension of 1 μm or less, which is far from sufficient for MEMS. Deep learning is a powerful tool that improves the TSOM performance by taking advantage of additional intensity information. In this work, we propose a convolutional neural network model-based TSOM method for measuring individual high-aspect-ratio trenches on silicon with width up to 30 μm and depth up to 440 μm. Experimental demonstrations are conducted and the results show that the proposed method is suitable for measuring the width and depth of high-aspect-ratio trenches with a standard deviation and error of approximately a hundred nanometers or less. The proposed method can be applied to the semiconductor field.

A laser three-dimensional (3D) projection system is an auxiliary system in intelligent manufacturing. It works with a positioning system in practical applications. This study proposes a calibration method for laser 3D projection systems based on binocular vision. The significance of the binocular vision positioning function for the calibration process was analyzed. Two calibration methods for laser 3D projection systems based on the binocular vision positioning function were proposed. One method involves simplified calculation models and another used data to solve the conversion relationship. The experimental calibration of the projection system was performed using data to directly solve the conversion relationship. The experiment demonstrated the simplicity of the proposed calibration method. The calculation time was less under the 3D laser projection system based on binocular vision. Moreover, the mean calibration error was 0.38 mm at a working distance of 1.8–2.2 m.

In this study, we propose a frequency measurement and estimation approach based on a lock-in analysis for precise frequency determination in polarization-modulated ranging signals. In this method, the modulation signal of an electro-optic modulator (EOM) is manipulated to introduce an intermediate frequency (IF) component into the detected signal. Subsequently, the detected signal is analyzed using lock-in analysis techniques to extract the necessary frequency component, and a new swept frequency waveform is generated, facilitating the frequency acquisition and distance calculation. We conducted theoretical derivations, simulations and experiments to validate the effectiveness of this method. The research findings suggest that our method can enhance the accuracy of frequency measurements by a factor of approximately ten when compared to a direct detection approach, leading to a corresponding improvement in ranging precision. Furthermore, even with larger sweep step sizes and smaller modulation frequencies, the proposed approach can achieve superior ranging results.

In polarization-modulated laser ranging systems, noise-induced waveform distortion and shifts in extremum position severely limit ranging accuracy. To address the difficulty of conventional filters in balancing noise suppression against preservation of extremum features, this paper proposes a local structure- and noise-aware spatially adaptive Stein’s Unbiased Risk Estimate (SURE) wavelet denoising method. Within the SURE minimum-mean-square-error framework, a local second-derivative structural feature and local noise are introduced to construct a spatially adaptive threshold-adjustment mechanism at individual wavelet coefficients. This mechanism reduces the shrinkage strength in extremum neighborhoods characterized by strong local second-order variation to protect key structural features, while enhancing shrinkage in relatively flat and noisy regions to improve denoising performance. Experimental results show that, relative to mean filtering, Savitzky–Golay (SG) filtering, and standard SURE wavelet denoising, the proposed method markedly improves the stability of extremum positions while maintaining the signal-to-noise ratio (SNR), and reduces the ranging root-mean-square error (RMSE) by 27.3 μm, 21.5 μm, and 4.0 μm on average across all tested distances, respectively. Extremum stability and ranging error exhibit a significant positive correlation (r = 0.863, ${\mathrm{R}}^2$ = 0.745, p < 0.001), confirming the critical influence of extremum structural stability on ranging accuracy. This study is of value for extending precision measurement systems based on feature extraction.