Influence Mechanism of the Surface Topography on the Positioning Measurement Accuracy of Micro-Vision Pattern Targets

In micro-vision-based positioning, designing special patterns as positioning targets is key to improving the positioning accuracy and measurement range. Many scholars, however, improve the accuracy by processing different targets and designing different algorithms while ignoring the influence of microtopography/nanotopography distribution on positioning measurement. From this perspective, this article focuses on two micro-surface processing methods, ultraprecision machining, and photolithography, to explore the mechanism of the influence of the surface topography on the positioning measurement accuracy. The influence of the processing technology on the positioning accuracy and the optimal processing parameters are obtained via exploring the influence of the processing technology on the 3-D topography, the influence of the 3-D topography on the imaging grayscale, and the influence of the grayscale distribution on positioning measurement. The experimental results are consistent with the theoretical analysis. For ultraprecision machining, there is an optimal value for the cutting speed and the positioning accuracy is proportional to the cutting depth. For photolithography, the positioning accuracy is proportional to the surface reflectivity difference. Under the optimal parameter configuration, the standard deviation (STD) of repeated positioning can reach 1.69 nm.