Multiple-inputs Dual-outputs Process Characterization and Optimization of HDP-CVD SiO2 Deposition

• Published in: Journal of semiconductor technology and science 2011, 11(3), 43, pp.135-145
• Main Authors: Sang Jeen Hong, Jong Ha Hwang, Sang Hyun Chun, Seung Soo Han
• Format: Article
• Language: English
• Subjects: 전기공학
• ISSN: 1598-1657; 2233-4866
• DOI: 10.5573/JSTS.2011.11.3.135

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top SiH4, Bottom SiH4, O2, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs)based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently,NN process model with two outputs were established,and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling;however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD SiO2 deposition process, and the NN based-GA method showed 26%uniformity improvement with 36% less SiH4 gas usage yielding 20.8 Å/sec deposition rate. KCI Citation Count: 0