Measurement and Characterization of Micro Corner-Cube Reflectors Array Using Coherent Denoising Interference and Physical Model-Based Neural Network
摘要
In modern industrial design trends featuring with integration, miniaturization, and versatility, there is a growing demand on the utilization of microstructural array devices. The measurement of such microstructural array components often encounters challenges due to the reduced scale and complex structures, either by contact or non-contact optical approaches. Among these microstructural arrays, there are still no optical measurement methods for micro corner-cube reflector arrays. To solve this problem, this study introduces a method for effectively eliminating coherent noise and achieving surface profile reconstruction in interference measurements of microstructural arrays. The proposed denoising method allows the calibration and inverse solving of system errors in the frequency domain by employing standard components with known surface types. This enables the effective compensation of the complex amplitude of non-sample coherent light within the interferometer optical path. The proposed surface reconstruction method enables the profile calculation within the situation that there is complex multi-reflection during the propagation of rays in microstructural arrays. Based on the measurement results, two novel metrics are defined to estimate diffraction errors at array junctions and comprehensive errors across multiple array elements, offering insights into other types of microstructure devices. This research not only addresses challenges of the coherent noise and multi-reflection, but also makes a breakthrough for quantitively optical interference measurement of microstructural array devices.