Method for improving dry etching end point detection based on change in time accumulation correlation of plasma emitting wavelengths

This study proposes an optical emission spectroscopy (OES) analysis methodology to improve the ability to detect etching endpoints during high-level semiconductor plasma etching processes. Representative etching endpoint detection methods using single wavelength intensity or multiple wavelength intensity ratio changes include a low signal-to-noise ratio, high plasma instability, a small etching open area, and weak by-product emission signal problems due to deep etching under high-level process conditions such as high aspect ratio contact etching (HARC). As a result, it is difficult to detect the etching endpoint due to the very small or noisy change in the intensity over time due to the process progress of the wavelength selected by OES to detect the etching endpoint. In this study, a method of deriving an accumulative time correlation value according to process progress between selected wavelengths was developed by selecting all wavelengths observed in a specific wavelength region such as ultraviolet region in a spectrum emitted from plasma during a plasma etching process. After classifying the entire correlation signal groups derived as a pair of two intensity peak wavelengths into a dynamic time wrapping algorithm, the intensity change rate of the signal according to the process time was observed by selecting the signal with the most sensitive time change rate during the process. During the vertical nand flash memory manufacturing process, a test wafer for the purpose of detecting the etching endpoint was manufactured and evaluated under the conditions of the cell metal contact etching process, which is a high-level HARC etching process. As a result, it was confirmed that the signal selected by deriving the time accumulative correlation method had a high intensity change rate and a signal-to-noise ratio over time compared to a single wavelength or a plurality of wavelength ratio signals. The method proposed in this study is expected to contribute to process optimization by contributing to improving the ability to detect etching endpoints in high-level plasma etching processes in the future.