Three-Dimensional Topography Measurement for Confocal Microscopy With Arrayed Laser Spots

Confocal microscopy plays a crucial role in topography measurement. However, conventional confocal microscopes usually have high-speed moving parts, which cause vibration, affect the measurement accuracy, and limit the scanning speed. Many studies have improved it, but the designed microscopes still have problems such as low photon utilization, high dependence on sample surface properties, and high complexity and manufacturing cost of the entire system. In this work, a 3-D topography measurement method for confocal microscopy with arrayed laser spots was introduced, namely, Lattice Confocal 3-D Reconstruction (LCTR), which can quickly measure the morphology widely and cheaply. First, a mask pattern with a pinhole array was designed specially, and the optical path of the whole system was built. Furthermore, a high-precision fast gradient positioning method was provided to locate the position of the arrayed spots on the imaging plane. In addition, a loop Barycentric interpolation (LBI) method was proposed to obtain the dense depth map from the sparse depth map more smoothly and stably. The experiment indicated that LCTR exhibits exceptional ability in measuring 3-D topography, with an average measurement error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.1~\mu $ </tex-math></inline-formula>m under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\times $ </tex-math></inline-formula> objectives. LCTR showed great versatility and measurement capabilities for different magnifications and sample morphology. LCTR has the advantages of low system complexity and cost, high measurement accuracy, stable and reliable repeatability, and high-quality 3-D point cloud data, which can be well applied in industrial detection and measurement.