Nanotechnology is the science and engineering that manipulate matters at nano scale, which can be used to create many new materials and devices with a vast range of applications. As the nanotech product increasingly enters the commercial marketplace, nanometrology becomes a stringent and enabling technology for the manipulation and the quality control of the nanotechnology. However, many measuring instruments, for instance scanning probe microscopy, are limited to relatively small area of hundreds of micrometers with very low efficiency. Therefore some intelligent sampling strategies should be required to improve the scanning efficiency for measuring large area. This paper presents a Gaussian process based intelligent sampling method to address this problem. The method makes use of Gaussian process based Bayesian regression as a mathematical foundation to represent the surface geometry, and the posterior estimation of Gaussian process is computed by combining the prior probability distribution with the maximum likelihood function. Then each sampling point is adaptively selected by determining the position which is the most likely outside of the required tolerance zone among the candidates and then inserted to update the model iteratively. Both simulationson the nominal surface and manufactured surface have been conducted on nano-structure surfaces to verify the validity of the proposed method. The results imply that the proposed method significantly improves the measurement efficiency in measuring large area structured surfaces.
Along with the rapid development of the science and technology in fields such as space optics, multi-scale enriched freeform surfaces are widely used to enhance the performance of the optical systems in both functionality and size reduction. Multi-sensor technology is considered as one of the promising methods to measure and characterize these surfaces at multiple scales. This paper presents a multi-sensor data fusion based measurement method to purposely extract the geometric information of the components with different scales which is used to establish a holistic geometry of the surface via data fusion. To address the key problems of multi-sensor data fusion, an intrinsic feature pattern based surface registration method is developed to transform the measured datasets to a common coordinate frame. Gaussian zero-order regression filter is then used to separate each measured data in different scales, and the datasets are fused based on an edge intensity data fusion algorithm within the same wavelength. The fused data at different scales is then merged to form a new surface with holistic multiscale information. Experimental study is presented to verify the effectiveness of the proposed method.
Precision roller with microstructures is the key tooling component in the precision embossing by roller process such as Roll-to-Roll to manufacture optical plastic plates or films with three dimensional (3D)-microstructures. Measurement and analysis of 3D-microstructures on a precision roller is essential before the embossing process is being undertaken to ensure the quality of the embossed surfaces. Different from 3D-microstructures on a planar surface, it is difficult to measure and characterize the 3D-microstructures on the cylindrical surface of a precision roller due to the geometrical complexity of such integrated surfaces such as V-groove microstructures on a cylindrical surface. This paper presents a study of method and algorithms for the measurement and characterization of 3D-microstructures on a precision roller surface. A feature-based characterization method (FBCM) is proposed to analyze the V-groove microstructures. In this method, a normal template is generated based on the design specifications, and the measured data is fitted with the feature points. Hence alignment and matching of the measured data to the normal template based on the derived feature points are undertaken. After that the V-groove is characterized by some feature parameters such as pitch, depth, angle of the V-grooves. The method also provides an approach for the analysis of burs generated during the machining of Vgroove microstructures. A precision roller with V-groove microstructures has been machined by a Four-axis ultraprecision machine and the machined surface is measured by a contact measuring instrument. The measured data are then characterized and analyzed by the proposed FBCM. The results are presented and discussed, and they indicate the dominant and regular machining errors that are involved in the machining of the V-groove microstructures on roller surfaces.
Conference Committee Involvement (1)
Sixth Asia Pacific Conference on Optics Manufacture (APCOM2019)