Direct Simulation Monte Carlo Modeling of Organic Light Emitting Diode Deposition Process with Line Sources

• Main Authors: Hsueh, Yung-Li, 薛永立
• Other Authors: Wu, Jong-Shinn
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/94017267497996719806

碩士 === 國立交通大學 === 機械工程系所 === 104 === Recently, organic light emitting diode (OLED) has a self-luminous, wide viewing angle, high contrast, low power consumption, high reaction rate, etc. OLED has become an important research topic by the attention of academia and industry, gradually. Nowadays, how to precisely control uniformity of the substrate deposition layer and deposition rate in large size manufacturing process, it has become one of the industry’s top priority. In addition to experiments, how to use simulation software to simulate in advance to obtain an optimal process conditions and cavity chamber design to reduce high development cost and accelerate the development process are important. Therefore, the OLED deposition process is simulated by direct simulation Marte Carlo (DSMC) method while the vacuum chamber is with free-molecular flow. The direct simulation Monte Carlo (DSMC) method is simulating the Boltzmann equation using a large number of pseudo particles, which each particle represents the large number of real molecules. In this thesis, we use a previously developed parallel and unstructured grid direct simulation Monte Carlo code named PDSC++ to simulate the deposition process within a vacuum chamber with a complex geometry. The purpose of this study is uniformity and deposition rate at the substrate by changing the diameter of holes. First, we calculate the deposition thin film and hole size scale to adjust the hole size, and the average value as a deviation of plus or minus adjustment benchmarks. To control the deposition rate by adjust the size of apecture. First, the first optimization adjustment is rough caculation according to aperture proportion. We reduce non-uniformity to less than 3% first. After the first optimization to give 2.65% of the non-uniformity has been significantly reduced to less than 3%. So we do fine-tuning for last two. The non-uniformity of film was eventually reduced to 1.93% and within the non-uniformity < 2%.