Schedule Logo

Poster Session II

4:30 pm – 6:30 pm, Wednesday October 15 Session DW5 COEX, Lobby E
Topics:
PrevNext

Machine learning-based etch rate prediction model in plasma dielectric etch

Poster 45
Presenter: Paul Seo (Graduate School of Semiconductor Materials and Devices, Ulsan National Institute of Science and Technology (UNIST))
Authors: Chiyun Bang (Ulsan National Institute of Science and Technology), Taemin Kim (Ulsan National Institute of Science and Technology), Youngmin Sunwoo (Ulsan National Institute of Science and Technology), Hongsik Jeong (Ulsan National Institute of Science and Technology), Byungjo Kim (Ulsan National Institute of Science and Technology)

Precise control of dielectric film thickness is essential in plasma etching process, as it is a critical factor in the performance and reliability of semiconductor devices. Conventional thickness measurements are performed using ex-situ methods, which result in time delays between processing and measurement. To address this limitation, we developed an etch rate prediction model based on in-situ sensor data. A machine learning (ML) regression model was developed using Optical Emission Spectra (OES) data collected in real-time during the etching of SiO2 films with NF3/H2 gas chemistry. The process conditions were varied by substrate temperature, gas flow rate, and gas composition ratio. Since the ML model is based on sensor data reflecting plasma characteristics, understanding the effect of process condition-induced OES variations on the etch rate is necessary. Therefore, to interpret the correlation between input and output data in the ML model, an eXplainable AI (XAI) method was applied, enabling the identification of key wavelengths that influence the etch rate. The proposed data-driven framework analyzes the process mechanism by predicting the etch rate and interpreting the contributions of each plasma species, thus demonstrating the potential for real-time process feedback.

POSTERS (88)