The Prediction of Etching Depth and Yield of ECR-RIE Using Neural Network

• Main Authors: Chi-Wei Chen, 陳起偉
• Other Authors: Ming Chang
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/30700829994825425496

碩士 === 中原大學 === 機械工程研究所 === 95 === In this research, back-propagation neural network was applied to setting up a prediction system for Etching depth which was able to simulate electron cyclotron resonance reactive ion etching. In the semiconductor wafer fab, we received data files in the etching process including gases、gas flow、pressure( chamber )、temperature(chamber)、coil electric current、magnetron power and reflect power, etc. Then 14 data files were classified as the input of the neural networks while 3 data files, including maximum、minimum、average, were classified as the output of the neural networks. Neural network system model was set up based on back-propagation learning networks. In the experiment, the researcher took 65 data files out of 99 in the Etching process as the training samples of the neural networks. The samples which were trained for 1000 times were inspected. After assuring the frame networks, the researcher inspected the trained neural network system models and 34 data files in the etching process. The research proved that the neural network system was able to predict the quality of Etching depth in the process of ECR-RIE. By combining the neural network system models and the Etching rate, it helps make adjustment in the Etching process and it helps engineers predict the etch depth in a varied condition. It serves as a basis in making diagnoses and issuing an alert. As one of the parameters shits beyond scale in the etching process, the neural networks would issue an alert. It helps engineers to correct the parameter and offers referential data.