Prediction of surface microtrenching by using neural network |
| |
Institution: | 1. Department of Electronic Engineering, Bio Engineering Research Center, Sejong University, Seoul 143-747, Republic of Korea;2. School of Mechanical Design and Automation Engineering, Seoul National University of Technology, Seoul 139-743, Republic of Korea;3. Department of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea;4. Department of Information Engineering, Myongji University, Yongin 449-728, Republic of Korea |
| |
Abstract: | Silicon oxynitride films were etched in a C2F6 inductively coupled plasma. A prediction model of microtrenching depth (MD) was constructed by using a neural network and a genetic algorithm. For a systematic modeling, etching data were collected by using a statistical experimental design. The process parameters and ranges were 400–1000 W, 30–90 W, 6–12 mTorr, and 30–60 sccm for source power, bias power, pressure, and C2F6 flow rate, respectively. The root mean-squared prediction error of the constructed model was about 0.019. The model was utilized to generate 3-D plots, which were used to examine etch mechanisms under various plasma conditions. Depending on the plasma conditions, parameter effects on MD were quite different. For most of the parameter variations, MD variations were strongly related to profile angle variations. The effect of bias power on MD seems to be dominated by polymer deposition due to the variations in C2F6 flow rates maintained in the chamber. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|