Data fusion for accurate microscopic rough surface metrology

Abstract

Data fusion for rough surface measurement and evaluation was analyzed on simulated datasets, one with higher density (HD) but lower accuracy and the other with lower density (LD) but higher accuracy. Experimental verifications were then performed on laser scanning microscopy (LSM) and atomic force microscopy (AFM) characterizations of surface areal roughness artifacts. The results demonstrated that the fusion based on Gaussian process models is effective and robust under different measurement biases and noise strengths. All the amplitude, height distribution, and spatial characteristics of the original sample structure can be precisely recovered, with better metrological performance than any individual measurements. As for the influencing factors, the HD noise has a relatively weaker effect as compared with the LD noise. Furthermore, to enable an accurate fusion, the ratio of LD sampling interval to surface autocorrelation length should be smaller than a critical threshold. In general, data fusion is capable of enhancing the nanometrology of rough surfaces by combining efficient LSM measurement and down-sampled fast AFM scan. The accuracy, resolution, spatial coverage and efficiency can all be significantly improved. It is thus expected to have potential applications in development of hybrid microscopy and in surface metrology.