Extreme Ultraviolet lithography with a numerical aperture of 0.55 will bring an improved optical contrast for contact hole layers and via layers. This improved optical contrast should lead to a reduction in the number of stochastic defects for these layers. To quantify this reduction, an adequate inspection methodology is required that can detect, in addition to the standard missing and merging defects, contact holes that are only partially opened. In this work we demonstrate a technique that uses backscattered electrons to detect these defects. In the first phase the beam-settings in a top-down scanning electron microscope are optimized to visualize holes that have been confirmed to be partially opened contact holes by either voltage contrast or transmission electron microscope. In the second phase these beam conditions are implemented on a massive metrology e-beam tool that has an increased throughput and therefore can collect information on millions of contact holes. In the last phase we show how this inspection can be used to enlarge the failure free latitude on a 36nm hexagonal contact hole pattern and to optimize the litho and etch conditions to minimize the number of stochastic defects on product wafers.
As the development of Extreme Ultraviolet Lithography (EUVL) is progressing toward the sub-10nm generation, the process window becomes very tight. In this situation, local Critical Dimension (CD) variability including stochastic defect directly affects the yield loss, and it is very important to inspect/measure all patterning area of interest on chip for the process verification. In this paper, by combining Area Inspection SEM (AI-SEM) with large Field Of View (FOV) and Die-to-Database-base (D2DB) technologies, we show a comprehensive solution for fast inspection and precise massive CD measurement of EUV characterized features, such as After Development Inspection (ADI) hole pattern, and aperiodic 2D Logic pattern. Also, a big data analysis consisting of multiple CD indices output by AI-SEM, a new process window by multivariable analysis is discussed. Furthermore, Machine Learning (ML) -based inspection and metrology to maximize imaging speed, is also reported.
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