Line-edge roughness (LER) and linewidth roughness (LWR) in semiconductor processing are best characterized by the roughness power spectral density (PSD), or similar measures of roughness frequency and correlation. The PSD is generally thought to be described well by three parameters: standard deviation, correlation length, and PSD(0), the extrapolated zero frequency PSD. The next step toward enabling these metrics for pertinent industrial use is to understand how real metrology errors interact with these metrics and what should be optimized on the critical dimension scanning electron microscopy (CD-SEM) to improve error budgets. In this work, both images from multiple models of commercially-available CD-SEMs and from JMONSEL simulation of modeled roughened lines are used to better understand how various SEM algorithm choices, parameters, beam size/shape, and pixel size/scanning scheme influence SEM line edge uncertainty. Furthermore, how these errors interact with the above-listed PSD metrics will be explored, imparting knowledge for optimizing LER PSD measurement with minimized error.
The Analytical Linescan Model (ALM) is a physics-based semi-empirical expression that predicts a SEM linescan given a specified wafer geometry. It can be calibrated to rigorous Monte Carlo simulations. Unlike Monte Carlo simulations, however, the analytical form of the ALM makes computational times very small. Inverting the ALM to produce an inverse linescan model allows wafer geometries to be estimated based on experimental linescan measurements. Thus, an inverse linescan model can be used as an edge detection algorithm for dimensional measurement directly from CD-SEM generated images. Here, an inverse linescan model will be tested as a potential CD, LER, and LWR measurement algorithm and compared to other conventional measurement algorithms.
Chris A. Mack and Ben Bunday, "CD-SEM algorithm optimization for line roughness metrology (Conference Presentation)," Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105850G (Presented at SPIE Advanced Lithography: February 27, 2018; Published: 19 March 2018); https://doi.org/10.1117/12.2297426.5754454063001.
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