Improving Endpoint Detection Sensitivity in Plasma Etching With Small Openings

Abstract

We propose a method that enables real-time endpoint detection during plasma etching of small openings (the absolute area of the opening is at or below 5%) on a wafer. Traditional endpoint detection techniques rely on observing changes in specific wavelengths, which perform well for wafers with larger openings. However, as integrated circuit manufacturing processes continue to develop towards miniaturization, real-time endpoint detection in small opening areas remains a significant challenge. This method utilizes strategies such as hybrid noise reduction, dimensionality reduction, and interpolation to achieve real-time monitoring of etching status. First of all, a spectrometer is used to monitor chamber status in real time and provide spectral data. The wavelet threshold combined with median filtering was used to denoise the data, the SNR of the spectral signal processed by the mixed strategy increases by 37.87% compared to that of the noisy signal. What’s more, 86.96% dimensionality reduction can be achieved through the spectral data dimensionality reduction rule. Finally, an offline model was constructed using a three-time spline interpolation for the selected feature wavelengths. Compared with other strategies, the sensitivity of endpoint detection in small opening areas of the model constructed by the above algorithm is increased by 13.2%.